{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "9aMFvFjcoI_v"
      },
      "outputs": [],
      "source": [
        "# Copyright 2018 The TensorFlow GAN Authors. All Rights Reserved.\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     http://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "# =============================================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "35cp5a7vN9V8"
      },
      "source": [
        "# TF-GAN Tutorial\n",
        "\n",
        "Tutorial authors: joelshor@, westbrook@"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XSTQ5Flu7FMP"
      },
      "source": [
        "## Colab Prelims\n",
        "\n",
        "\n",
        "### Steps to run this notebook\n",
        "\n",
        "This notebook should be run in Colaboratory. If you are viewing this from GitHub, follow the GitHub instructions. If you are viewing this from Colaboratory, you should skip to the Colaboratory instructions.\n",
        "\n",
        "#### Steps from GitHub\n",
        "\n",
        "1. Navigate your web brower to the main Colaboratory website: https://colab.research.google.com.\n",
        "1. Click the `GitHub` tab.\n",
        "1. In the field marked `Enter a GitHub URL or search by organization or user`, put in the URL of this notebook in GitHub and click the magnifying glass icon next to it.\n",
        "1. Run the notebook in colaboratory by following the instructions below.\n",
        "\n",
        "#### Steps from Colaboratory\n",
        "\n",
        "This colab will run much faster on GPU. To use a Google Cloud\n",
        "GPU:\n",
        "\n",
        "1. Go to `Runtime \u003e Change runtime type`.\n",
        "1. Click `Hardware accelerator`.\n",
        "1. Select `GPU` and click `Save`.\n",
        "1. Click `Connect` in the upper right corner and select `Connect to hosted runtime`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "83-azWpoYsDg"
      },
      "outputs": [],
      "source": [
        "# Check that imports for the rest of the file work.\n",
        "import tensorflow as tf\n",
        "!pip install tensorflow-gan\n",
        "import tensorflow_gan as tfgan\n",
        "import tensorflow_datasets as tfds\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "# Allow matplotlib images to render immediately.\n",
        "%matplotlib inline\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)  # Disable noisy outputs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "b2xrX4F-OEL7"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This colab will walk you through the basics of using [TF-GAN](https://github.com/tensorflow/gan) to define, train, and evaluate Generative Adversarial Networks (GANs). We describe the library's core features as well as some extra features. This colab assumes a familiarity with TensorFlow's Python API. For more on TensorFlow, please see [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JMljl0ZwONgi"
      },
      "source": [
        "## Learning objectives\n",
        "\n",
        "In this Colab, you will learn how to:\n",
        "*   Use TF-GAN Estimators to quickly train a GAN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "pI8zy5Bz65pa"
      },
      "source": [
        "## Unconditional MNIST with GANEstimator\n",
        "\n",
        "This exercise uses TF-GAN's GANEstimator and the MNIST dataset to create a GAN for generating fake handwritten digits.\n",
        "\n",
        "### MNIST\n",
        "\n",
        "The [MNIST dataset](https://wikipedia.org/wiki/MNIST_database) contains tens of thousands of images of handwritten digits. We'll use these images to train a GAN to generate fake images of handwritten digits. This task is small enough that you'll be able to train the GAN in a matter of minutes.\n",
        "\n",
        "### GANEstimator\n",
        "\n",
        "TensorFlow's Estimator API that makes it easy to train models. TF-GAN offers `GANEstimator`, an Estimator for training GANs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qxrYrU887Mns"
      },
      "source": [
        "### Input Pipeline\n",
        "\n",
        "We set up our input pipeline by defining an `input_fn`. in the \"Train and Eval Loop\" section below we pass this function to our GANEstimator's `train` method to initiate training.  The `input_fn`:\n",
        "\n",
        "1.  Generates the random inputs for the generator.\n",
        "2.  Uses `tensorflow_datasets` to retrieve the MNIST data.\n",
        "3.  Uses the tf.data API to format the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "Zs8kdV0w7Rtq"
      },
      "outputs": [],
      "source": [
        "import tensorflow_datasets as tfds\n",
        "import tensorflow as tf\n",
        "\n",
        "def input_fn(mode, params):\n",
        "  assert 'batch_size' in params\n",
        "  assert 'noise_dims' in params\n",
        "  bs = params['batch_size']\n",
        "  nd = params['noise_dims']\n",
        "  split = 'train' if mode == tf.estimator.ModeKeys.TRAIN else 'test'\n",
        "  shuffle = (mode == tf.estimator.ModeKeys.TRAIN)\n",
        "  just_noise = (mode == tf.estimator.ModeKeys.PREDICT)\n",
        "  \n",
        "  noise_ds = (tf.data.Dataset.from_tensors(0).repeat()\n",
        "              .map(lambda _: tf.random_normal([bs, nd])))\n",
        "  \n",
        "  if just_noise:\n",
        "    return noise_ds\n",
        "\n",
        "  def _preprocess(element):\n",
        "    # Map [0, 255] to [-1, 1].\n",
        "    images = (tf.cast(element['image'], tf.float32) - 127.5) / 127.5\n",
        "    return images\n",
        "\n",
        "  images_ds = (tfds.load('mnist', split=split)\n",
        "               .map(_preprocess)\n",
        "               .cache()\n",
        "               .repeat())\n",
        "  if shuffle:\n",
        "    images_ds = images_ds.shuffle(\n",
        "        buffer_size=10000, reshuffle_each_iteration=True)\n",
        "  images_ds = (images_ds.batch(bs, drop_remainder=True)\n",
        "               .prefetch(tf.data.experimental.AUTOTUNE))\n",
        "\n",
        "  return tf.data.Dataset.zip((noise_ds, images_ds))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "t6aboJBr8Rig"
      },
      "source": [
        "Download the data and sanity check the inputs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 279
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 2639,
          "status": "ok",
          "timestamp": 1559656474241,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": -480
        },
        "id": "zEhgLuGo8OGc",
        "outputId": "efd62ab6-6d5c-4ee3-f6ed-85447922b54e"
      },
      "outputs": [
        {
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WPtqfzL5pNg/1OYEyd0KqNyd9+npuJ6Uw//JJSn1yhxk/fIRrExP92k8kKce+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\nTZdeTWnYOYyuDbVoZ65gXkxKkURDJoh0DqvM9DG9CfXyYH2PjzHc9Tp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dz86JJ3nWWoaO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\nTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fef6cc15750\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow_gan as tfgan\n",
        "import numpy as np\n",
        "\n",
        "params = {'batch_size': 100, 'noise_dims':64}\n",
        "with tf.Graph().as_default():\n",
        "  ds = input_fn(tf.estimator.ModeKeys.TRAIN, params)\n",
        "  numpy_imgs = next(tfds.as_numpy(ds))[1]\n",
        "img_grid = tfgan.eval.python_image_grid(numpy_imgs, grid_shape=(10, 10))\n",
        "plt.axis('off')\n",
        "plt.imshow(np.squeeze(img_grid))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4sAetutZ9t93"
      },
      "source": [
        "### Neural Network Architecture\n",
        "\n",
        "To build our GAN we need two separate networks:\n",
        "\n",
        "*  A generator that takes input noise and outputs generated MNIST digits\n",
        "*  A discriminator that takes images and outputs a probability of being real or fake\n",
        "\n",
        "We define functions that build these networks. In the GANEstimator section below we pass the builder functions to the `GANEstimator` constructor. `GANEstimator` handles hooking the generator and discriminator together into the GAN. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "oZ9n-jw_MG6C"
      },
      "outputs": [],
      "source": [
        "def _dense(inputs, units, l2_weight):\n",
        "  return tf.layers.dense(\n",
        "      inputs, units, None,\n",
        "      kernel_initializer=tf.keras.initializers.glorot_uniform,\n",
        "      kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n",
        "      bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))\n",
        "\n",
        "def _batch_norm(inputs, is_training):\n",
        "  return tf.layers.batch_normalization(\n",
        "      inputs, momentum=0.999, epsilon=0.001, training=is_training)\n",
        "\n",
        "def _deconv2d(inputs, filters, kernel_size, stride, l2_weight):\n",
        "  return tf.layers.conv2d_transpose(\n",
        "      inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n",
        "      activation=tf.nn.relu, padding='same',\n",
        "      kernel_initializer=tf.keras.initializers.glorot_uniform,\n",
        "      kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n",
        "      bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))\n",
        "\n",
        "def _conv2d(inputs, filters, kernel_size, stride, l2_weight):\n",
        "  return tf.layers.conv2d(\n",
        "      inputs, filters, [kernel_size, kernel_size], strides=[stride, stride], \n",
        "      activation=None, padding='same',\n",
        "      kernel_initializer=tf.keras.initializers.glorot_uniform,\n",
        "      kernel_regularizer=tf.keras.regularizers.l2(l=l2_weight),\n",
        "      bias_regularizer=tf.keras.regularizers.l2(l=l2_weight))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "NHkpn6ks90_R"
      },
      "outputs": [],
      "source": [
        "def unconditional_generator(noise, mode, weight_decay=2.5e-5):\n",
        "  \"\"\"Generator to produce unconditional MNIST images.\"\"\"\n",
        "  is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n",
        "  \n",
        "  net = _dense(noise, 1024, weight_decay)\n",
        "  net = _batch_norm(net, is_training)\n",
        "  net = tf.nn.relu(net)\n",
        "  \n",
        "  net = _dense(net, 7 * 7 * 256, weight_decay)\n",
        "  net = _batch_norm(net, is_training)\n",
        "  net = tf.nn.relu(net)\n",
        "  \n",
        "  net = tf.reshape(net, [-1, 7, 7, 256])\n",
        "  net = _deconv2d(net, 64, 4, 2, weight_decay)\n",
        "  net = _deconv2d(net, 64, 4, 2, weight_decay)\n",
        "  # Make sure that generator output is in the same range as `inputs`\n",
        "  # ie [-1, 1].\n",
        "  net = _conv2d(net, 1, 4, 1, 0.0)\n",
        "  net = tf.tanh(net)\n",
        "\n",
        "  return net"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "w-ZqQ4_thIrP"
      },
      "outputs": [],
      "source": [
        "_leaky_relu = lambda net: tf.nn.leaky_relu(net, alpha=0.01)\n",
        "\n",
        "def unconditional_discriminator(img, unused_conditioning, mode, weight_decay=2.5e-5):\n",
        "  del unused_conditioning\n",
        "  is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n",
        "  \n",
        "  net = _conv2d(img, 64, 4, 2, weight_decay)\n",
        "  net = _leaky_relu(net)\n",
        "  \n",
        "  net = _conv2d(net, 128, 4, 2, weight_decay)\n",
        "  net = _leaky_relu(net)\n",
        "  \n",
        "  net = tf.layers.flatten(net)\n",
        "  \n",
        "  net = _dense(net, 1024, weight_decay)\n",
        "  net = _batch_norm(net, is_training)\n",
        "  net = _leaky_relu(net)\n",
        "  \n",
        "  net = _dense(net, 1, weight_decay)\n",
        "\n",
        "  return net"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OhTAjxnyPS5e"
      },
      "source": [
        "### Evaluating Generative Models, and evaluating GANs\n",
        "\n",
        "\n",
        "TF-GAN provides some standard methods of evaluating generative models. In this example, we measure:\n",
        "\n",
        "*  Inception Score: called `mnist_score` below.\n",
        "*  Frechet Inception Distance\n",
        "\n",
        "We apply a pre-trained classifier to both the real data and the generated data calculate the *Inception Score*.  The Inception Score is designed to measure both quality and diversity. See [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) by Salimans et al for more information about the Inception Score.\n",
        "\n",
        "*Frechet Inception Distance* measures how close the generated image distribution is to the real image distribution.  See [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://arxiv.org/abs/1706.08500) by Heusel et al for more information about the Frechet Inception distance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "1jF-FW5LPTn6"
      },
      "outputs": [],
      "source": [
        "from tensorflow_gan.examples.mnist import util as eval_util\n",
        "import os\n",
        "\n",
        "def get_eval_metric_ops_fn(gan_model):\n",
        "  real_data_logits = tf.reduce_mean(gan_model.discriminator_real_outputs)\n",
        "  gen_data_logits = tf.reduce_mean(gan_model.discriminator_gen_outputs)\n",
        "  real_mnist_score = eval_util.mnist_score(gan_model.real_data)\n",
        "  generated_mnist_score = eval_util.mnist_score(gan_model.generated_data)\n",
        "  frechet_distance = eval_util.mnist_frechet_distance(\n",
        "      gan_model.real_data, gan_model.generated_data)\n",
        "  return {\n",
        "      'real_data_logits': tf.metrics.mean(real_data_logits),\n",
        "      'gen_data_logits': tf.metrics.mean(gen_data_logits),\n",
        "      'real_mnist_score': tf.metrics.mean(real_mnist_score),\n",
        "      'mnist_score': tf.metrics.mean(generated_mnist_score),\n",
        "      'frechet_distance': tf.metrics.mean(frechet_distance),\n",
        "  }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kxF2-gWHHaej"
      },
      "source": [
        "### GANEstimator\n",
        "\n",
        "The `GANEstimator` assembles and manages the pieces of the whole GAN model. The `GANEstimator` constructor takes the following compoonents for both the generator and discriminator:\n",
        "\n",
        "*  Network builder functions: we defined these in the \"Neural Network Architecture\" section above.\n",
        "*  Loss functions: here we use the wasserstein loss for both.\n",
        "*  Optimizers: here we use `tf.train.AdamOptimizer` for both generator and discriminator training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "OBd8Vg7lHit8"
      },
      "outputs": [],
      "source": [
        "train_batch_size = 32 #@param\n",
        "noise_dimensions = 64 #@param\n",
        "generator_lr = 0.001 #@param\n",
        "discriminator_lr = 0.0002 #@param\n",
        "\n",
        "def gen_opt():\n",
        "  gstep = tf.train.get_or_create_global_step()\n",
        "  base_lr = generator_lr\n",
        "  # Halve the learning rate at 1000 steps.\n",
        "  lr = tf.cond(gstep \u003c 1000, lambda: base_lr, lambda: base_lr / 2.0)\n",
        "  return tf.train.AdamOptimizer(lr, 0.5)\n",
        "\n",
        "gan_estimator = tfgan.estimator.GANEstimator(\n",
        "    generator_fn=unconditional_generator,\n",
        "    discriminator_fn=unconditional_discriminator,\n",
        "    generator_loss_fn=tfgan.losses.wasserstein_generator_loss,\n",
        "    discriminator_loss_fn=tfgan.losses.wasserstein_discriminator_loss,\n",
        "    params={'batch_size': train_batch_size, 'noise_dims': noise_dimensions},\n",
        "    generator_optimizer=gen_opt,\n",
        "    discriminator_optimizer=tf.train.AdamOptimizer(discriminator_lr, 0.5),\n",
        "    get_eval_metric_ops_fn=get_eval_metric_ops_fn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "n1uldXfUfstT"
      },
      "source": [
        "### Train and eval loop\n",
        "\n",
        "The `GANEstimator`'s `train()` method initiates GAN training, including the alternating generator and discriminator training phases.\n",
        "\n",
        "The loop in the code below calls `train()` repeatedly in order to periodically display generator output and evaluation results. But note that the code below does not manage the alternation between discriminator and generator: that's all handled automatically by `train()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 2281
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 221607,
          "status": "ok",
          "timestamp": 1559656706482,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": -480
        },
        "id": "AH6gcvcwHvSn",
        "outputId": "a72e2218-95a8-4585-8a5c-7c4ec896ac0c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time since start: 0.32 min\n",
            "Trained from step 0 to 500 in 25.67 steps / sec\n",
            "Average discriminator output on Real: -10.51  Fake: -10.17\n",
            "Inception Score: 5.97 / 8.38  Frechet Distance: 98.58\n"
          ]
        },
        {
          "data": {
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hQJpURqocfekk+1szbyKVXZEOFNFoHOJxQKE8GzQd5TQJihWC0ydQZoA/XcM2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\nqGuapjUQHeqapmkNRIe6pmlaA/lfIuBBue3jEt4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fef7ca32a10\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time since start: 0.69 min\n",
            "Trained from step 500 to 1000 in 33.82 steps / sec\n",
            "Average discriminator output on Real: -6.00  Fake: -5.53\n",
            "Inception Score: 6.61 / 8.38  Frechet Distance: 69.39\n"
          ]
        },
        {
          "data": {
            "image/png": 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YySTOmqOQM3R27D/Fj8U+vvbn72fZPQ0UhccJb4vRH0oXTNyyRp6+iRC9m6uo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\nplnEDHWTyWSaRcxQN5lMplnk/wFqeWzN5+xZfgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fee56571790\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time since start: 1.05 min\n",
            "Trained from step 1000 to 1500 in 33.28 steps / sec\n",
            "Average discriminator output on Real: -20.26  Fake: -20.98\n",
            "Inception Score: 7.15 / 8.38  Frechet Distance: 58.26\n"
          ]
        },
        {
          "data": {
            "image/png": 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+dzaVIVyFaL0dnHlgH31n+oi9hgfCpaIbBl27ujnqOk59QQFlBVaEOxcUM3Lj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\nGcQIdYPBYJhB/h+KQl8g3Fsp2AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fee3f248e10\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time since start: 1.43 min\n",
            "Trained from step 1500 to 2000 in 29.00 steps / sec\n",
            "Average discriminator output on Real: -28.74  Fake: -29.66\n",
            "Inception Score: 7.35 / 8.38  Frechet Distance: 54.52\n"
          ]
        },
        {
          "data": {
            "image/png": 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iClmw+2csg9bRIC3H2qmpURENzahu/7msXkYHsUf6c9Y4ZWjMy9CIm4ZEhMxg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\nLkmSNIf8L+JFEmJm2STdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fee53e1a310\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time since start: 1.79 min\n",
            "Trained from step 2000 to 2500 in 33.04 steps / sec\n",
            "Average discriminator output on Real: -27.47  Fake: -28.54\n",
            "Inception Score: 7.17 / 8.38  Frechet Distance: 62.29\n"
          ]
        },
        {
          "data": {
            "image/png": 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jIZrqamncbab8aiPClc6GJXaG4nOgLYe+kV6UYBCnsLDaPZNr17iZs3YmomTW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\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fee3f2306d0\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time since start: 2.14 min\n",
            "Trained from step 2500 to 3000 in 33.26 steps / sec\n",
            "Average discriminator output on Real: 1.75  Fake: 2.23\n",
            "Inception Score: 7.38 / 8.38  Frechet Distance: 55.90\n"
          ]
        },
        {
          "data": {
            "image/png": 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x2IU/FpqSBfZ0aEgOJwP8x6sHWZPIYulVgsbmLGavmUvp/gCR1Mikiv29vlip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\nnznt/hiKlmQVEkWWkPOsxT8DLJ6DmW/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BoSNsdkBgtJ+kr6+N/ujE+4e8G+WubKpK5+Oq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\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fee3d4299d0\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time since start: 2.51 min\n",
            "Trained from step 3000 to 3500 in 31.49 steps / sec\n",
            "Average discriminator output on Real: -2.78  Fake: -13.50\n",
            "Inception Score: 7.38 / 8.38  Frechet Distance: 58.81\n"
          ]
        },
        {
          "data": {
            "image/png": 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B7HgIafZCbm50cavTg1AdY45CLYHw5XHVTZdw5YIyrGNNaCd6ad3soMkSJBWF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\nus1ms40i/x/HKN8JI5IGjgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fee48486ad0\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time since start: 2.86 min\n",
            "Trained from step 3500 to 4000 in 32.85 steps / sec\n",
            "Average discriminator output on Real: 84.46  Fake: 79.25\n",
            "Inception Score: 7.63 / 8.38  Frechet Distance: 56.05\n"
          ]
        },
        {
          "data": {
            "image/png": 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CCAlLCIqdMYqdMZjk+9IZkocZeTWIsqlYwSh+LTyhJ8mz4ZQUVLsTVAf6GWMt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\nt97+DqmbmQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fee235bc310\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time since start: 3.20 min\n",
            "Trained from step 4000 to 4500 in 33.85 steps / sec\n",
            "Average discriminator output on Real: 139.49  Fake: 130.57\n",
            "Inception Score: 7.48 / 8.38  Frechet Distance: 59.30\n"
          ]
        },
        {
          "data": {
            "image/png": 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2RkUmRePmufl8ZtMyZq9bCTYnIuYmb4YVTBZEMoHRWEP0XBtvN1moGemm7+gZ\nFhbYuXWGDXX+GoTJhtlsR3sn9evS0ISKS7OikKoYFFYnBQsWkJeZjuKwIsorUtelmVITNxIgdPYQ\nj508Q8dIcFS7gwrFxrWufNRp0+G8L10m4/Sf7OL45tPURvpJvo+/VSDwWOystth5cNYc5qxbhXBm\nnK+sFMh4hO5jbdRsOYt+Cd+bRKLrCaS/H1x2uvUw3XoYk6JSmO7lC1+6k7XXTKe7bpCXnt/Fm7t3\nszMafqdT4+1LKrh56TR6DI1f9cWJjoOWCARui50HKlzcuHQFc9atwLuoGGF1YfS38uyvD7KlpoGW\nxPg08UoYOo0DffzwjZ1EMmysu28JmSV5CNP7u1/ee8Hnv4OeLtqOHOeZE30MNJ/i1ZM9tPvCJAwd\nKY1R9/UWCNba7GyaX0n+zStQPDkpQTd0hredYs9zr/Orgwd5cyhC7BJ2aWNFIPBqDjakl7HmtulU\nFpbgmleGVpoFioqUKgM7h9k1FGfXkI+D4UEa46OvTHapg1hiPfQO+GjzKczILOCTd1VzQ3YBhS6D\nWO053m4J8dxgIwf2dtIw0s+ikQz0sixIK3pvTORDMuFFPWnohMIRAn1BjPMZE8JiJ1UGLQGBsDq4\nftkcYoNBjIYznEoOjPuqf+3UDB68YQFLr1uNUlSZah4WC6XEMxrE6G3HaDhNsq0f/2shhsUAsQIv\nVBam3DCJOLK1ntqRdnpGWRavCUE6Ggx1Ie0aQtVQsnNRsnOQsQiyuwOZiKPkFSMycoj1j9C++TCH\nh9sIGKMLyJW7VVbmW1K+7/PIsJ/WM+3sP9Dxvr1bTCjMt2Yzd0Yat06bwuINy1DKZ/4mFdJIMnCs\nhd3P7efl7ccvuSw8EUvQ+uY5nHcVMy/DxuJ0G71Jwd2zcrj+3uXY9SAtNTWcOHGCV4M+DGmgKSpr\nPDYeXj2bZTlp7D94iueCPmJjbJ0A4LCYuHd+IV+4bjZT1q9GKZ8OQhD3D3Pw5QP84tWTHG/tH9dM\nG78R462hZhK/eoW4lmDN4iLySrIQObkIqzP13F1oE3ARcTC6WjmzYwdPv7Sbn9cE6Q+PPXfaLFSm\nWzKZNt3F/VXTWXPNapwbZqdaJUiD4N7T7HliG7/Yc4zXYxH0898LpFxKEtCNsTcbU4WCIsTvxEry\nNAfr8kp56JrFLLlvPub8YggF6Kpr50zTIN2D3XS+VsNbkSitRpTBeHDU1c4Ahw63UFY1m6LSbG6+\nfh52PcxSXSB+AAAgAElEQVSnPnEtlr4+AtuOcPitJp5s9vFktBMpJXbNgjm3EJGZhYxGR2UATHhR\nNykqFRl2vrDQjcVmAaG8kxtNMga6jrA6mHX/PMzxCMFAgMb2AKHk+AZJ//C62ay6Zm1K0M/3NpHx\naKqqMh5HBkOgWbAtrWbDwCluyvNiWrkcpWImiTj0nmim+80XOdh8kt5RrvyKBDUap/nAMax9Qewm\nG7qRxK9HSfr70Q/sJich8G66HfuSbAZ7Irz5Qgex2OiFxJVjkD39ve4s6euj299JrfhdH70qFByK\nmUKTnS9Wzmb9bdlkrVmMUjoj1YBNSjB0jK4Ojjyzh6c372JH+NIrHGORJFt+dZac1Su4c1YJ1rkV\n7G7y80C5G6swkL5eppv7mZUWx9ZrRlVgepqFv1hUQaW3mMa3Wzj8xCt0hYbGLCBuk8byfA9/df8i\nMlbdgMgqACThgQHOHK7lP368nyNdbQQvQ+A+LnV2+hsJ/txg+KCFxUuKSFtUTVlRCWpxKcJkRioq\nF7P3okdPsXXz2/zzwdEFAd+NIgRu1cRMh5P7qpaw8f4SclcuRsktQ0oDGY8Rb+rm8H+9xGPHT3DW\nLVmSXY6uqihSYviGENE4umEwnExQPzR6d4wqFPKsVgrNZgKROCAJSBV7hoVVuaXct2AxSz43B8WT\nQ0fHEMOnDrBv20me2tXN8WD7Jbs7fh+PnwgyzW/ntjsXMX9WAUbdIRS7Ff9rb3NiRydPdfp4KtqF\nlBJFKCzIzaBk5WLCJVPp3n5kVEbAhBf1NLOT4qIpmNYtAfP5LIlYhKR/gMRAB4wMYqpchmJxMO22\nKSzSu3ntJ/3U+TvG7RpMqoZpzS0o5VW/eVGAWlCZ6gwpBGrRDCCVFeJZekPqdcCIhOg6cppffO15\nvj98DP8oqgcv0JcI8qu247T9S4Qoe5iFk5FkhDei7QQSEXRD52tpc7l/vsaUxTrNcR/fiZ8jPAaX\nlEzoEPmt7bFvkKS/n6SSxHzeAjQkCEWQaXez0FnA3WlF3PyHJdgXr0Zk5CEULTVF9STJwAjBn/+S\nl/YdZmt0dJlAYanzaKSO6wcbKVqznGVBL9q3X+ZnB2x8dWgYR3YpUT0PezSDWVlOXGbJoyutZD10\nH/ufa+HRp3bzbKB3HCxCQXWWjcfXlWO95i6EJx0QJAc7qd2xjz//v9s4NNgwpjTWD8NhfzNHDyhY\nj7SwJusYP15aiOcvv4aSmZ2KP1zEUu89rdJ3OtVwbSz3QQVcFhtrMzL492n5ZHzrWrTSaQiLAykl\nMh4n3tZG25/8iG+dO0OTlDyyfhn/549uT7VLiIVIvvIsiZoGMCQHIgnu2dxIKP7BsZqLYVY1bs5w\n8XVvBkG/Dc0keSvhZs4dZcy7bT4ibwpGMEC07iD/9u09bDleR2dkCP0y1Cv0xHwEjDjCbEMKE3p3\nFzK5j9bjBj/qHGZLrBtDGqnT1BwOvrUmh6VFFrbvbuRnP9w/qsZmE17UFSFQbS5E8YzUQRiREWJb\nXmPLqwf4QesI8WiYP8iq58a/2oi3soisnErKtBrqxnF8r82DWZDqOnihyFq5+K0TQiDPzx+pJ+h8\n/TSvfPfXPDp0jJHk2INBMSPBvqF6DCk5hYIhJSGZQAiB1+5h+SMVFC3OSnW6G2gjpifHNKawmhEe\n+3teU6bO5Zr7bMzMzaD/GRNCSOr8aRSuSKP0mgps5XPxeLzYs6wIq/039yoeJtTWynN/u4NfnTrJ\niWHfqLvxSSSheIxk3QnkzHKyNlQwmyiBHx1CszrA6sDz4I3ccf0i1kdDqHYXmRaDyDOvcfBAI0d1\n37j4tm8vdPKtGxdh+dwjCHdaaifp62Hry3v5l0e3cny4/bIL+gUMaRBJxDgyrPD1swm+03EOj9uT\nCt5fhPyb8ikcKER7pnXULTRUofJZZy733LSc4k+tJN3hQisufGdMGfIxcPAMO7+9m8e6e6lPxHmk\nwMnDUwpQc6ek3DJWB9rGB1DXR0AaVHe18bzyS+5+qR7/KHaZSWkwMGKmNz+Pyj8uRp2zmtvNTiwu\nM8IqiJ9uovlfn+GrXR2c6hpgOBK5LIJ+AZmIIeMRejtj7Hg8wKY7dEjEURUFs2rCYbIyzZvLv/3d\nPcyoKEfWHqVl/04O+1tGNd6EF3V/PExXaACZiAASdJ3nG/r5ydFmjvpCSGnwuO8UC4Ir8ar5KJoF\nbdy6W6Tc9qFElODTLxEf6UDLc4NQEdPmpdwJRvJ8+ZdE6glkeARZfxLp9/PsqUE2727mbMs5BpLj\n015TIgmd92NfsPkFggyzxt9OdzJ74UzMOdn49jfR/qvjYw5C1fcI3qxRuPFuA0jFNLC5cE+ZjsOd\nSfHMalAUpsQ0HDlmnIXpCHcmmFLl60IIpKGjdzXR/PYhfry1mR2HTtAQHCQ8Rl92Qk/yDy+d4E8L\nK7n2phxyN8xnTX4B5vR0hGZCzcogzWPH1dLM4Muv8PUGP72nznK2b4TOcSjTnmPPZe2S5ZTddy1K\nbj4IBaP5BI/9ejc/e/kIJ9o7iIxz4dsHIZEMxWMc6u0h8tYu3HllCLsLLuKAMaVZMHkuLvgfBoHg\nflsRd965nvn3rcBWWZA61+B83YERi4CewG7RmeVo5fNVKg9aC5i1aD65y2YjY+FUab5qAqcLxZmq\ngXDYrFTcdCMFu/1E+vre0y/mw5DUdfaFBwm2wPytUb5sNuGwmmg6KzjYGuRwfwvnDp9gfzRCRE+O\n6ZDnDyKUiOJ/dTeRLA+exfNY8uXbseTFsWzdznRbBn0WK2npBl9cV8DMihIGnj/Nc/sO8dS5dkKj\nfHYmvKhH9Tj+4CCypw1yygFJS0TQFJbE9QQOTWVFXpw0uwqqRkAm6NHHr52lRBJKRHlmzwn8I0mc\nWZ5UAMV7GpPFhtR1kkYS3dAxkgmIhZEdjchgiLeaAxztDY2rj+5ieBULt3hKufH2ZaRPmQqqRlPH\nAHvPdI/ZGq0fCvHG6Sauq9mPmDIbYbWngqY2F2qRE1thMQgF24WJIZR3zmtFylRPmO4WTu89xNNb\nDvP04XZ6wsPj0sxIItnbNMTyLbVM92nkTEknf95UCA8goyoy6KO7fpAjexrY98YunuoMMhJPjCnw\n9W6mqC6m5pWjTqtMZQZJg8Pb6tmy5RiH6louW/uBDyJp6AwGAzTtGsK9IYQjVwft4oaOlJLRaJom\nFKY78rjtzuuZe88KbFWFCFXlQrhTSpE6jcxix1ZaTPl9qygP9RE/3c7pviRPvVlHYnsdDtVCieLA\nLTTcms6AYnAiFqGxpwl/dHTplBJJlx7G7zPwHTWx0NCIajrHGgPs7xjmTMLHUGx8e/68H0lD58TZ\nbl574RjlIcGc6gxEdhk5d6/ghv4QM9GxO3RWlpnpeamOp17aw9Ot56gdQzfVCS/qEklyJEjiRD3a\nzFWgmpmZ52FtvpeOHo2SLPjCxmqy8rIRqhmfHqU97hvXa0gaOs/0B2nZ04xTtXJa99MeGcSimpBS\nEjeSJPUrdbzze7GqJqoyvHxm8UzcN9yA4vVidLbQ2NXAHn3sD25fIsjbLad542lYtiKMo7IYNScn\ndWCJEAgllZ55IQBKPIIR9GP09JEYTkJ8hNOnT/D0rtM8fayP7vDQmK/pvUgO7G/AeqaD4mkmLOvn\np6osVQ2G+2k61su2g/3sCfouKW3yw1CYa6Iw15qKFxg6Rm8rW3bWc7J58KoJ+gUiCZ2X6mJou5uo\nSs/CVZLznvQ4aej4W4P421K73UvFIhRuT/My7+6leOaW/pbP/nz/Es2MSBiEpEqTvZTO4SC+cwHe\nau7haEQSlUk8ioUq7GTpkKnF6FB0dsbjNIZ7x3ymrm7o+CMRWk/G2aMHORLqoTvqu+zHG74bgeDt\ncJyeA2eZ2trG+vZc3JVLmb9yFguJsWBoCN9InD2dEY7+fDO/HGqmcYxprxNe1AH0wQihHS1Ybo+h\neNzcvKyI2cNz6a6JMG9mHPsjjyBcLkhEicSC+C/DQcOhRJSdifdmaVzJh+Ni2BUTlZl5rFs2k7kP\nTUHNygShEDl0jK6399IWvvQCp99Glwanevx8+cmT/MfuTgpvnYVjyVJcuaW4zSqKWQMBgQjEYzGi\ngX4iTbXEdu4ieCaJohn8Z6+PFwZGLovQ6dJgR6STHRGgB9h9ZtzHeD/yZtjInWFN5c1HI3TufJ23\nWo/RFh1db/jxJBVIbiT6g9f4jMfBvHvdYD0fG5ESf/cQBzc3cmJX16h2LiYhqbYGsQ8MoQ9loDgs\n76QtypER/BEIxmNEfd20HjrNr753hO3BJgZjwd95Dg6Pxwe+CAlDJ5QIsiw9zrZ+H4Px4BWfsxJJ\nc7if5nA/O4bh5/Umypyn+Jtv3kel3k3y2EnONAb4l0ZBra99XBr/fTREPSkI+1Q8g10oTjvqvFWU\nzVpGiS5RVeV8VgwYIwPogYHL6iObSKx2lvDZjWu55kurULOLQNWQkQBnawWnz5rHbOlcIGnotIYC\nfKIpgvuHwxQ/VsftbjufyNaxF0gQ8PLpTOpHVGrjAxwOtOELj2Doqe8hKeV4Fm1OGI7vCnKyPMDS\n+TH8/X3870drOdN5eZqDjYZIMsZT4Ubmn9vFnDYXasXi87Udkhf+7xZ+8uYbHA2PLkssYEi+2h/g\nW3/5GGtuLsJz4yyUkhnISJjEr37Oz/YKNnf3Ux/qIRgNk0gY7/Q8uVI4NSvzcsooeqCY5C/7iA9d\nXSNMkuoYWefv5MFv/FuqiNAwMCQkDcatv/pHQtRrYxHuqasl44//k79fmsec61ahVExPHYJhdoOe\nREYCHHzmBMc2N2JW1fE74WgCsyg7xOJSgTkzDxSBDPvY/J03ePLVnewOjW//+FSANkEkOIIvHKFj\nSOGJTrCcUUmzOugLDjAYjeFLRgjp8XHzW09ktgVaKa95nYXPtxE82U59dwvhxJUNjH4QgUSE//fS\nIV49OsC1M/Zz21IrDz95kvrTnfQH/eijfEZ0adASHOSboRFczzahvXkodWCHYSAH+ukNwnA8QdRI\nXDUjSxUKVkNB1rcRCwQua4bLpSCRxBOXb4H5SIh6SBqcCAex1DTwtwPtZJ/uRmRmIRQNxWzFpGjI\nZIzO2m7q2jqvaNnx1WTrgA/H1lNcP2AlJyfAv9cN8fbOM5zu7mRQH30+/PtxodovbMQIJ6EjBkpI\nwaolMAyDuKGPS5rgR4UhPcqxs/3sF3YKk10Eo6EJt5gZUtLaH2BouJ6W9m521Wrsru0jmhy7wMX1\nJE0kYSACA6OrN7hclJnTuK6yitvvmY89Uo8wtfKbmtWPNx8JUb9ATE+wtTMBnaffeU0gMKnqhY4B\nGIYx4SbW5eKQL8LIobM01ffg9Qb5aUuIoWjsin5+QxoTzjq9kpzuC/DjWCfl7hHC+sSwBC9GIBnl\n5ECUk2MPs0x4hBDMzHXywLIiFqypJPpcPTYsmBWN6BU4nexq85ES9YshkePmO/4oUpfwUTfgg/8B\nk3Ui0h7309E/guz/+FuAHxVUoZDhUsjPSGAMDOLbP0hV1EaLxUlzLEB8jAV5E52PvKiPhQspXvJ/\nSGB1kktDEcqHciddLoGYKM/nbx8+ctWu40O2MxBCUNdq8PJr/dwQ38W5NoXrdRcBm2BECPoifpJG\ncsztESYqQl7Fb0s15V+toSeZZJIPiUUzkbjMlZfjjUko2FQNqwWSkVTGTxRJdAzthCcKAlAVlVjs\n4o3Y/kdb6pNMMskH81ETdICENEgk44x8DD2zFxIW3o/xa5IyySSTfCz5qAn6B2FWNKZk5vKtP7qf\nWeUlmN6nOd9E5ve5jSbcp1FRmGLLpDItgT+m0hBI0J0Y/ckjk0wyySQXMCka5TlZfHJDNV/41CYa\nT/XQ2THIcHz8+kVdbSacqFtNGvfOWcB9VQYtIzZe6gxwONiGlBIhVKSeSJUiCwUE6PEEbe3DBOLR\nCZXK6FAt5Ga7cZshMOzn3MjHP5VqkomJANIUE7kOJ9ZCDyRjdHYH8YUjl9wB8aOMSVEpTXNx68Ip\nfPmzaxEyRoZixa6aufqNHcaPCSfqNoeJh7+6mOzpMymzuVhnJJGJKCT11EHDwz1g6Kk+3aqGr7uH\nP/rT59neVMtwbOKstnM8RXztkevYUGSw89mt3Lq1+Wpf0lVFEaApqYU4npw4i+/V4EqXwFg1EzfZ\n8/jqsmVU/L+NyN4mvvXPe3j+4EnaggNXvHz/t1ERo65svRS8VjcPzC7kKxsrUApnEN/9AkO+TiLG\n5Te4FASKAEUBjFTrjMt11yecTz0ZSHDkm/vw17SDkQSLA2F1AQZG7R4SLzxP8tfPY5ytQVgceKbN\n4p++/7+YP2cqllEe6DzemFSNT6Ulqc7OIBbJYqReoIgJd6uvGIpQuHd2Pm9/ZS2v/t29eN1OxEUP\nWPt4Is7/0xSVLJubMncu2fY03BY76rvOfr0c2E0WvjItg2/+r5WU//19KJkFKFOr+aOvb+LrN6xj\nmbPoso7/+1CFQobZwcNZCyg0uy/rWJqicr/byr3z5qOtvInokI8XHu2ltmmA4DgfffnecTVy7enc\nnDaN/6xczN4/qGbrrHJWpmdi00bfy/73jnlZ3nUMBPUE/9R4jAd/aGdt5TGGVNg6oNM40kF8qAc5\nMIwiIP/tEVYsCnPLLVPJL5nJn83x8p12K2/0XP0WAapQKLp5AWkzixg+3IvPZ/kfVT7/2yy15nHD\nrFVUrp9Df0MNDpOZIRFGH2UAThECp2ZlvaOEJe4wudctxDSjBNnbjjxzCpHjRW/v5cWzfrZ3hwgk\nIlclz1oRCi7NwjpHKWurNbxVhVjySjCZrCSGeogfOcoPT45wpN93Wc4vVYXKAkchC69ZReHGVai5\naaAnkLEwWYU5XHdzJa2hdt7e2nHZ+6IIBOlWJ18stVG5bilq1UwUsw2zZqJUc3NTx0n+6/k32Xbk\n3GUZv8CeyYyNq8m/cRlDPRF2/tc2fl5XS3NgmMQ4VwJ7THYWeou5/44ynFNnYnVm4FVsFNktZOVB\nfEOEv2qo4fubD7L1aOO4f/cTTtSTSA4lhuDAYY6flYygc3BE0hUdfqdlpyIEGe0JGrtCJOKDbHo4\nj2XrFrGwNcDR4VoGr1AD/IvhMpv43MI8ylfPR8T91DXWsiMeGvdxLKoJt8lOhWKlSlWweyS2ZVNR\nvHlgshCva6Nlfw2vhsKEE7GrVmRRaMvguhk5LJlXhGHxMrK7g3h09NeTZrIzu6SE66+bw7y2PqLt\nQcxBB6X2dMrXZiBnFSK8Xox+P9ktfhbVNVNXe5rnawev2H0wKRoVmouluTmUrS9lQcEC5lVZ8JR4\nIT0ThAqBYYzpRYiC43x/Rw17mnvHfeFXhOD6fI2qWWWoxcUYff2E3txLfb/C1GunkzujgIKqIszb\njhC5jKJuUU2UuDO5f3Up9y2dTdHyxSglJanDVgwdhML0ARuvHT3L9qON455tY9FM3LW0mAUb5mLo\ngvpn3uCn2/ZwJNpNMDn+z4QEzFYTG69fRdr02Qib6/xPBAiwTpEsq/ASbB8m2tjNK4OxcTU6Jpyo\nX+BQpJtD79OTypCSgXiA3W0tBF6C8qJyZiyfyuJVAU50+Xj9TP1VCZpaFBNlaV7++O51ZNhNnNx7\nkGcPHODN5PgdDKEIhUyzk+rpOZQXlrLIkc1ykwm318B56wKUgnKE2Ur04GlqsjKx9/Xib65nT7uP\nweiVD4qtS0tj3bIiCss1es/U88budvyRyKgnboXbzgPTilhdOYOTQw28Foji23qMyq5W5m0oxZyW\nizdmIkPJZ+HcfFbPyqSu2EIyeJQz3UmaYiME9PGdRBcwqxrZmpMKj4lrC4u4eWk1ZZ+YicgsQISD\nGMNBAqd76Q0rDCtxFhSXsmmjieF4hEAoxPHe8W3bK5EsmKZSmGNCRkIM1J5ix882c3wwnYfzdFyr\n5mFOy8WhWYhcpv49Ts3KtOw8bl+zgC/cUYGzYjbEEgRPNNA3ZOCXYWZmK5iyPMzxupiVbuPk0Pid\nhyAQ2DQLN66bTmVZBvW7mnj1pZ3sDHWN+6Ep74wpDUxSILyFCLMNwgH0ngH0oQBauhmluAwlq5A1\n1XMYqO+hfl89DbHx04gJK+ofhoge40x/B89+dzef99q4bkU+/oEKTje10xodf+v49yEQZLncrJtT\njf3Ge0lsfoL/fvFtfnKya1wmqiIUnBYThRkuFuRU8aefqmJa9Syw5yDjIFVJd9wg1jsC+hDW7HSm\nfWYT/5+vC3lA46Enz7CtfZiwHF9hVxBkqTYsioFPTxB4l8VnVjXuKLcxZ9EcQlLh4PbX+Ef/IGFj\ndPfDpphYWmhjtd3P09/azLf9Jwld+Dy7QN29lzSLg2pXCXMMO8vXeZi6rpyMihl895ohXjmWyTPD\n5zjb28NQIExIJkmO08Q2KQozs9NZnVXJ7SWCuatLUVbMJxC1MlJ7hnj9cWInGmiqjbOr206NNcA/\n3lNE5d338dBGA+EL8c1X68btLFsB2FUL5lkVCG8aemsjZ/bt4y/a/JiVELc2nmDKnGLcZge5mpMB\nxjdt+P9v7zzD4zjPc33PzPZd9N47CHaQIFglsUoiKZGSRcvqohUrsZzkxD6ucS7nJFGO7dhx7OMS\n2XG3umV1kSIpir03EARJgOi9Lcr2Ojsz5wdE2Sq0SHApS7jm/ssFZriY75nve8vzCkCaZGJmRj4b\nVi3lc1++EcFoxt0zgPfIAVr3dHKoUeKCOcSXFsSYtnEl95cnEq0u5J/2thNS4xNGFQXIlKyYC6ej\noXJ0uItf+zzXTNABsiQLn7DnYjZa0dQY7tYmnFv24jvVgn12Msl3PER6ZRnmFQup9Rq4u1vgZz0N\neNQIshq7arX4WIu6pkFIkTmsjHJX1E9Raik3F81ASWvg4f4PV9RNkoG5c8p59LHPYrLZ6NwfxdMu\nx23nlWK2c8vsMv7rkQUY5q3GKAGj3fhePoRvxwViyWE+0xbkgnccVdNYYMvhrqRc1qT5SLunkkcy\nzASGz7ErMhiX+4GJhZtkMPO1xGpq7CF+5unjD74xFE1FEkSKEjJJ23w7pnlV7N9+jp/uD+IK+yf9\nnSxIKKR2di09s6185+UnCb3rBaVoKmNhH2+Ez/MmAj98CXJ2nmZjzSy++cXVbLovh9uCHk7/+iwv\nvtnIlmg/Pb6ROHwPAuk2M09/sozM65diSEtHFMF5ppvXv3WcV7RhWv2DeCIBIrKMrGgIosB9v3Xz\nu2UbqKm9gUVddh7Y/zw/8Jy56vuBicTgvORikmYvR0jOIHjkOM7XuxgOuBAEgbDPC4pGpSGZlYZM\nzjEQl+texGQw8lf2PD69cQ35X9yAaBSJHdnCc79oZ9uFbhoDw4wF/ciawt43jDwr7uG6B++ldH0V\nC+qf5MB4S1zuw4aBz1kqKHBkw1AHgc4mxsLXtu8l1SizMnUMk0GASJAXt7Tyq2fP0eTpxVJvZMPO\nJ/jqY49QPreI0qU53O2tQnxM4tlIO13+USJXOQvi4y3qaEQUmc7gMG/8Tx0JpFG0pJKl4U/y6He2\n8x1/E4Fr+Eb+U5YZkvictQCTyYjacZr/cl5gXzg+HtNl9iw+uXQaD6wvJhaR+d4/PU39WB++oIeo\ny0d03I8qqXREVEKxCaE7Fu6mzTPIbwdF/vEXiUx/aDnle1QO7RiL2yBsi2SiIjuPm76+nOycbGY/\nc5Bjr++jOzhCptXMDxfZqSrM4OgrTTz/zG7qXN2Tjhsnmm2s21DJDTONNJw8RejPPPgqGioaMQX6\nvS7qT1+g8/sOir5sx2xUmb0iibx5c7lrvIrWp47yxaE+vJN0+pxtyWTTrJnc9FczyE63ENp+nu3d\nURoiEiM+H8dcrfRGfQSVKIqq/vGFpsKAz01ooBUqMigsVFi4IIq0R/yzLeCXi0UTuS3mIEMBtbeF\ng23n+anHj6KpE5VHKUlgtZBtcjHL7iOehdoGUWKdo4wVm28k944FiEM9+F95lfqDKi+2nOO0f4yQ\nKr/9fQRkAbJSEZITmZ/o5q70MAfiFI0wO4ys/OJ0km1j/Oj5On6+79qXFkejIqOjdtI1CQwm5ikm\nZkclzmgCoirQGhwnGA2CEkVKySCnpopNNzQSO1rE46EgvcrV6cbHWtRhws/bHQnw3IWzDD4tcPtI\nJQuzJdZtyGf7zgBnxgcIKNfW77vAmsaS65ew4KE1REMyh35dz8muIUbj4N28yFHA7Ruu4+YbyzHE\nPPzP8/W8eKyZ/oCbiCKjor1vfNivRPErUXyCkYwKlfQ52WQ355BisjMYB1E3iBJVOcl87bZp5C+c\nBofqGOttxRX1kyBZqM0oZ86dN2OLjHOs4QQHulonVTomCSIVjhzuXVXALesW4B2ROdJw+VMtZU3h\nvGeMb5w8SdL3BxAMApZYjOXVJdy6eBaF9jT+4Sd7+O1YJ33qlQ0WqbRmsvH6xdx5z3JyKhPZ+csD\n7N7XyGmnF6ciEtZkxmT/Ja2hIzGZ/ufP40kpJbGyiLTV15F0qO+qTjMXUQQ4gZfY748wqgxxpKWV\nhsCEWIiCiGAwgijiUQz0yfErrbMIErfby7lr883M3liD4nTT+OIenm9o5mxXmIbQKL636sIFINlk\n5KuVKZQvvg4hNZPzri62jsdnJ11mNvGF4jwKrq/l3GuNHN7bSff4tT/B+zSRBtVMWSyGweCgfF01\nD5nHqT4G/m4DZbcUk5tunkiYm6wEVDsN3SJH/YN4lauvhPnYizpMzNBsjIwyduIUo+4+xmans1B0\nsLk2j9+cljk7MkIgdm2EXRAE5hsdLK0shrIMdv3+CM9sP0nvmGfSC1MATJKRTFMim66bycaNtdis\nJnY918TTR1rp9I/+2ViwJIgUGBzMyUinakkGpRuqMcpjKJ6xuOwCAbIMdq4vKOfmdQsRUPj9rmMc\naLyALxZlZnoqdy/Mxz63mp0vv86uxkb6ole++8hzmLhjRhalVQvZuHYGObNmcuTNLppHr6wfYUyV\neU3CkgcAAB1mSURBVNU7BG8OARPVGO2jQSSLmbUzi7h3TRXbdzrpG718UZcEkevnlbLhtgUUz8vF\nVX+aJ3eeZN/gIJ7LXJgaGttPtlO8oouF1bmYSipJMFlxR/xMNo8rCSK5RgsrLYnkWGPsP3KGOt8w\nA2H3258REYi0u4mNhtAU0OT49VBYzEbuWTeXxbfNx2GLcmrbMR7fcZzXQgFGwp63E+QWyUSxzcKG\nPAeb77mZ5Dk1jF8Y4/iBFvZ64jO1KyvJwqdqC7CkZbP3zBu09o6+Z00aRQNZpgQEBJwxX1ympo1p\nUXaEh1k/1os1NYHE+SUszFzF3KocvN2QujwDQ1YaSAYIehns6+WJ5hGOBAYIxWEjOCVE/SLDEQ+v\nNPppbXPy+fQ87vjbcgKJSYQOn+dsb/c1MSYyiQbml+QwJ81Mf109v/zJS+wabyZyFYkeQRAwSUaK\nHJlcf2MRBZUp1O8e5MD2Ntr8ly59ExCYW5hCRmYW8xLy2VhWTPU9ZYi5FfS+vJXulhbc8tUn4gRB\nYHpONjfVLEAonoFy+jC/aunlhDdMotFKVX4mG24pQlSDPL6vjaNtI++ZIP+B10CgPCeVb96/ErFm\nIVJOGRjNoF36+CwgIIkTAmWXzBgFibAqv+eEEFFk9jV0IIYDrE+PUfCpCqxn62D08ieNpJgdLLqh\nktlLi4mOeeh9bS8HRlvxXGE89DBB1ka9LIyEEBGwiKa3GrOu/FmVBJGSnHQ2lJfwiCMLU2SEvz7X\nwZj8zt2pikrwzAjygJ/0BBPl2RrEK9xhNVJ9z3QS8pORDx3gRN0pnvA63xZLgyiRIdmoyMtmzawC\n/m5GOtY770Rzj3D+tWOc23vhip+VS2FIsWO7oQJiAU6Hh+hX/vg9mASJSoOD7NIUZuWUIkhGGkZ6\nuNA8wGDEe1VDoF2xMHvH22g7dIaZSelYs7IQC8uwFlVgVdWJXZsoIQgiWsiPx9XHyaiTkCLHJQP3\nsRN1gzDRgScycYzU0Ij+ScY4pio0hN38ozfKIsciPvO5anpFjbY/DBGIc5G/gEBBejpln7kJW5md\nzqd+z9FA91X7aaiaRigWYVT24QnKRANBgqofl1l+X0EXmOhiTUt28P0HlrFw9XKE1GxQFcSULDRN\nY+uBMOfaI3GphU6UzMxfVsbKz8yHaAx51wFElxdJEMmxJjCjcBpizUqijacZdzkJTSLcIwoCYmoW\noaW3YjDZsQhGRFXFLmikvasJU0DAZjGRZLdhN1lQYzLTrJlYFAttvhEavO/1nVY1lZggIKSkIZbP\nR7C9ftlDEyRBZHZaLrm5FQgJaYw1udm1z4AqSwhc/sIUgLm2TPJSChAcKQiMIwmT67Q1CxKZycnc\nv3YRX374JgSDBfXUXiLd7USG3/miUVQVTQUEEUelnYwVKdD4/r/3Sph4qRoQbIkgGoh0hwl0BYjG\nYghMVC/lZqRzU0IJn1y/gEUPL0ZMzYVICNeWl9jV1MhB1UMsDs1AoiBgSEpDmL0YzTWAL+h6OwFp\nNkiUJybxr8nzWfI300haUA4WG90NPfz8W7v5Rc9JgleRrNTQ8PkjPP7TRh5MSqN4XilWRypWRyJi\ngn1C0C9+2JFEUl4ZC9Lz2DPQQjQOL7SPlajbjGamO/JAFEgQjOQbEggoMjvdzQTk8DsWpC8g890f\nnedL3ygg2+gg25xMuzwUt3sREEi02PjuP29m+ZJKXtl2mH/Z2oY7HIhLxUtMVWjzDPDfvzyFYEyj\nthDuuc7AS39472cdRivz8kr40bc3UVRaDAPtKGeOIiSkIa7ZhNp+kq7AICOxYFx2QbeYcrklcRpC\nUjaE/BhmFFBSF2Ik6uWWGYl8dmUiimyk6VsNeNrdk3qRKJpK67kevv3pX1CEhQ3/uIycmbmUW0dY\nVeTi584/fjbNmsAD6xfx9w+tQjRaULvOIfX28d0dbeys/zO7b3siQkU1hL1osehlC3pxYhZfum86\ny+ZnQtjP6HgXe0QPeQkZyH4nwcsI9QmA1WjhdovMTKsBLRpCGemZ9KJenVLJQw+v4IYNcxGMEmp3\nE2r/AGrk/e/FYFEQJWXiRgzxsSrQ0IjJYdSGw5Cdi23DYhzjw0g/eREzEvenzWXztz5F2bxyrHYb\nos0EkRDy60/w3R1dvNwywlDIHZf1o2oaiiAgmKyobY1IoeDERkEQWJCfzBMP1JKy9gFMKXYY6UDz\nu8gvTOQzt1l45pcGQv6rG3kXUmWeGqvntX9voTQ5hztSkrinppCUr9yHYE1EkwwIgGBLYlpFAd+6\n2c7aZwyMBq/eBedjIepG0UCeJZFHjFlUP3wdthlFGK2JWCQzPo+P4L8/yZH+ZryxP4nFaRohn4Jm\nTsZgtmOIo/eKKIhkWSx8uzSVhUV5HN7ZypbnTjIWjM9O+CIxTeXgQBv+x2UeuG0pN95xKy+Fg8RG\ng/ygLYQYTWJVVRqLFieTlJlNQc95/u3ZN2nqGWH9kpn89acXE/AF+cWPT7CrsRln5OoTUAICVTUW\nqmptCEYziBLG1Zv439M8uN0eMuwiiQVZaAaBrNQRUu0S5piJyCR2686gl+dajmNFpOHHbjZvXsH8\nNBM50/KoaTPS4OkhpioYRQOJQR8Z7WcIjYfYekhm91A7B/uHCFwiOWszmEmzp4DBSujJ51GGnO/7\nuXeTZDPxnw9VU3PTCsyp6XQcamPb7+o57elBVpXLOqWJgkiGycLXE3O54W/vILG2ks7jA+x+vIH+\nwOgVNc5ZBQP3JlZx5+c3MGf1DBzGEGpfKwS8CMVFrMiKMORspj0y9sefMZhwrKzCWJaBr8WH8834\nNb5EgwpnnxphYamThNnF3Lp6PhXWIGJKBoVli8mfWYQl0YYgCCi9PTifepqv7GngaLeH4eDkm9Le\nTbo5gbLEXATRROxcO0G3h5iqsL4ina/dXkvWutvAqnLoZ/vZ0VBHvjHIvVW5JFTmYzAYEAQmnde4\nSFCVUXxBuuURxvPN2OakgGRA9Y1CwAPWBARHCsbMHLI3fooluxX29TbjucpehY+8qJslIzOzU3nk\nhgpuKJhLRmYQrb+DsNeKLBuRIkH8Yf97FoJZEFhpiZAixPDHwvjjkFW+iE0yUZVWyIqHbiYpMETd\nseOcbGuPS5Ll3bjkAMda2pm208HNpqWsues21GAMRhQEX4xpjGNWXOxoGOe5zrO82OQkEDUyr3Yh\nqmbG9eTLvHDoJO3jI3GxWdXQaHXFaBn0UOMdAZMVklOpVML4Or3gN8M0K8hRzGJswiZ5ki+6qKYw\n8FaC1X/mLAnPW5FzkvG0K3hiwbd3Ur5oiJ3nOxhzDxD1hWnoMXIhOI5HCV+yc3SmwcqtlmTCqsjj\nR/oZdH/w85ErWrg/qZzrbl6Do6CYrbtP8uozu6k/24orcnkVKxbJSFWCnc1FyWy8dS3pK+biOTvA\n/mf38fK5xisKVSUYLMxPL+TeB25kzvJKLM4u6o+f50CbE0WJ4kelzjlCVFSxGkzIqoKAQL4tHces\nWUiZ6Qyd9nB+KH4bkWAsxmNNrbQ/fYgb7zdTUlFKXkYyWO0I9mQwCqAqqJEg3e1t/Gx7PTu6RvDJ\n8V07mZKNacZkIpEYextg1KtiMZiomF7FvNXLiZrs7H/8GM9uOcCh/i6us1pZ4xYwK25ishy3juNU\ncwLr5uayel01poW1CJIR1ePk6JZGRlwqRbPzmLusFHvldP76/qV0Pz7C2Z6re7l95EW9zGThUyVF\nfHLTDRzriHDw8FG8zUMEnQJy1IgsKTT7R95hnykJIsk2G9dfV0iSRcEVcsUlQXjxdxekJbFp9RwS\n162i8ZmXOHOhhcGw+5pZEwRjEU41NPFEVKHw3uu4weag1mahX3NzrqeLcw2tvNwoc8E3gKKp3Fie\nzoLCFJxONy/84Q2axocIx6nqBeBg+ziONxsZEwyYzA76ox5iY/14D3RSVZ7P6uoMYoYUznmseGUh\nLruv4bCbbUdO47KkkqSpyKoC2sRLxi+HONIT5kjP5Q2BTjRaWVBRwuqaUkIjA7zijOKMfvDP5WWk\nsHnjKuxlsxk+0c5rz+zl2YNHJ+7lAzBLRvINDmYV57CqtpAHpudjvnkhckcfB1/Yx0sHT9AQvfxG\nKIMoUZyewoM3VjPv7lqM/Z2c3rGfZ/c2sbU/iqKpeGNBwrEokiAhCiIGccKb6I7iRLLyCkAU6QiM\ncUiOn2V1FJUd0SHEfY1UzcmnuGQeATGF1sN99ATOsHR+CRmFhWgBD329XTzR68Uvx9++IqYpRJQI\nkWiMHf0CoxFYkGyhtrQIsaiSUH8/T7x4gN39LYiiSEp+IeGCYlqOnCUSufquzovc4LBz3+LpLFy+\nEDGnBNnnpWlPG79/5RRdgx7WdmYzK9mPedkaVt1Ww7LdZ3AOuBm8ir/JR17Ua1Ic3Du9jFD+DH7w\n1e9xpKcZ31thFlEQkQQR+U92oAICKSYTC3PSSdu8kjF3hGHnOME4eVs4DBbmlhbw6QdrUIdGeX6/\nk1Od/sta2FfDiaiP0+fqyfm3Vh5NTSMccnBUUzkYHaU9MPyOz65bkMfaOekccbv5Vr+HYBwFHaAz\nNMpzR05yvKEbG0aOutqQNQVBEPhsjoXVcoSIoPJmwIZHMyJOMvn3blpDI/TJXrKsSWSZkhgKud8u\n7bwcMTdLRrIwMis7h0Wr52C+oZD6/YcYCbouyy7AVpxOyZfWINjM7P/tPpqPNX/g311CINNgp7Q4\nk5XpJay9fibVd0xDcGQS7Rig6XdbeOpEAzuvsOQzwWChuiSPux6ci5icTPuTR/jt3iZe7HLhjry7\nFjuGJIqkGs0sSUnib1fkkp6dgrtvlLOtTZwM9F/Rtf8cAmA3WgmhEOpsY+hYlLo+hVd/epQ6ZZTH\n/nUjGVnpBMf9DLUP4ApfmxkIfTEfpyNOFDRaYm4CapT1RRmsL0slEhPpaB9m13grbiXEjbllrLxl\nAYY5Jfz+Hy4QjtNSlkSJTZlG5pSVIGQVEvMFGDp6gcd/cZwt/Y24tSgZTaOs2mtkWnUtQlout5XP\noKW1l0HnFBZ125xMUtbmMtpZT91Y69uCDhMVDO+OYVsMBhalOPjx9BQc6Xn88vsvcepId1ySLwIC\n+ZZk5mXPQMieyejD/8b+hsYPzWcmpir0hrw81H/p2LgoiEiVcyE9i0jDIbzvWeDxYSzie48bplGU\nMKXkIOZNQxseJaAp+OTwJZtvJkMoFqHL56RbGEESxA+sWBEASRAwGQwUJmXxBSmXNaszSa+IcmLP\nQR752XG6vM4PzIUIgoBoNCPaU9A0jafDndTJl45FC0wMBUkx23gkbQH3/K9q8mor0BIyUMJBosf3\n0Pufx/mXoX4OBa48NFhoTGJBWhViSTVoKt8/F+W1bu/7CPoEkiAy127jR+WFpD34MEJSIoeeeJMD\nr7a+Y1N0NQhMmNpNT85lVAnRd+gcZ/cc5997R5EEkWVZlVjTCsBs51x9J1teiF/hwruJKrG3iidU\nIkIETdAwzi7FMKuY9tYRfvLP2wgFophEA7cuS2Xt4iQOuf286mkiEgd/JIGJ4SSJM22YCxJAlHCd\n7WbPV7bwlPcUbiWCIAhsGQoh1w/xm5YTiHNWsuzeQl5yprJne++kT/4faVEXBRFRiYEmIOWUk2pL\nxuMLv2d3JAoiFoMJi8HIJ8oS+fL6uThuvwut7QSH+07SFRq7xBWuDIfJwvLV07nv75YQ0ySaxlMI\nyB+NwRwXMRuMGI0Wuk55OPyba+NNfSmyLSlk29IhJiMPNNMcHsZ/jbp5NU1DQ8MoGVBU5ZILoNqa\nzd3FhaxZKWG58Q5S7SlYR1vZfaCeH7zeSq9v5LKS2wlGK5nWpIlYq6pgNhgxSNL7CqLFYGKaLZN1\n00q4+4EK0qqWkWiLoHZeoOHgLl465uWguxVnv5MhWZ6U7W2xFmOpGgE0UFUMiH92EEuROZWl1YtJ\nfXQTUnoG8tbfc+jkXo4G3lvuORkMokS5OYVPp1Zw891JfP7lM3y7x4WsKdgMZublFPGdr62hbFYx\n2kgPzR1H2OXviMu13w+zZMRhspGUaOeXX1nJ53+sINb1EbJuxe9IoE/1k+NIZbo5g9Kq69Bkjdiu\nLXELoWpASI4SbPEhO4OYKkJE5BBOo4jRYELS5AnvqliU8YF+oruOYKlahlg8m/Scs6RZOnGG3B94\nnffjIy3qmqbhbfLhfHOE1M/M4T9qU/nGAReNrj/Gx0VBJNls51PGLGqWZDFnVSn5M8vxu/t59Jf7\nON0zTDROzoQrpFTWZ5SRWlyIHFBRYyLaJB0HrxUPmrOpMafSGY5yZCw+L7PLQRAE1meLrMsWkQd9\nDP/mOO2j/XHpkLsUiqZiEU2YDUZUTUVR1bd9bawGE7eY07hjyVxqbp9LTloUrfUUzzZrnOjs4mxH\nN+f73ZcdNgvFonjDPrRIEMFo5gv3Xc/dFQacdcP0XxAIiQJhQSBbi1K4JI286jwKcnIpnZGPcm4f\nPz85wrnOXnp7x2gbDjMc81+VcVOjEmJrZIwZ4QCCPZkHyg0MNom83i+ivuXvYpAkLJKRVYZ0br2h\nlkUPr8GYk4HScIDvbznJ660DcXvpVhhTuGvmfD7x8AqyLN1oWxsYjEQQBYHq4iS+sbmGikU1GJQg\nf9h6jCd21uOSr90JN6LI+N3DCB31FNbU8vUvpJLuHWe8Y5yhnU5uJoncFJXCNaWUyX3sfaWfnxzq\njkuN/EVUTeXFYZH0Xic1vjFSStJY+Ug1R37tpX68D5ccQFZitLuj/ORUmL8Ly9hSkqg1Z1EnJPI6\nU1HU0egaC3OsZYyNPierr5tGU2uMF8NOOrUoNtFIkSWBNTPM3Fi1kIpFxdgLzDiHPLy+u5UXjrYx\n4otPy3GaJZFlC0qZW1OEYLaj+n2EBYEPxy7sgxEQSLMkcOMnllNakcn5s820ypN7KCZDvjWV2kXz\nKJ9fwbhP5tAZF2PBwIfiaz/TmMI0yUpIDbE/FmCalEjNghRuqpxO9ZxS1AwTh/eOcLjuMNs7wjS7\n/Xjl0BWF5GQ1hqdniNEn95G2aRGLFs1BK87AN9/NSJdKRFWISiKpapS0EgGLxct43wjbt0RprzvG\n040jtHkmEpfx+E56YmGOj/ainj2BtHAN1dfPYbN/HPPRTpoGVKZLVsrLZOwzplOTWcHc+TNJnZ1H\naKCLbS+c5MX6fto98asIq8o0ceuiDLIWl7P/F42MuSfq/meabDxQWMSSGxcjOBJoe+EYb247x+mO\n4WvS4X2RmKrQPTDGc88dY+0MFzUzc5BmFeAqkClKGqVMgCxrCNs0K2f3NvDykV72903e2uNS7HV5\nSdjXhJqRRu31s5mxuoqHwgqd9Ufpu+CidUwhlmmncEE5oskIkoEZCRrTHRqvT7K24yMt6gBtaoRX\nRwbIOdhA9YxiPrUymcTBcc4TIcFgpcqWzMZ5MazzawiPCzSdamRPXR1PvTnImM8ft7rxbEsyhZUO\nUkqNoCnEQh4aCRMQPhpj6oyixCx7NkW3LSQsaPSca2c8+uFNgFpqTqNszgKkaZWM1PewPRol+iGM\nkJPVGPlSAjdZ0kgw+MlKsLG4Yj7XrU7CkJBFuytI3bZ69r3UyWvegauKlw73j/D8z1+lyCozKzUb\nu8FKzJqNYVoMrxIhJKiMxsJEfaPE6tvpr2tnW6uRc+EhxiL+uLW/w8ROtH9wmKPbj7GwYi6G2XNY\nazaQVtDJ8XaNxZKN2XNCmGrmIKZnonhiOA+e5VTrBX76RhsdI764+QAJCGSkqlQWy4QVkf27h/G5\nFfKNCayvqODO5UvR0osZPtDIlj+c4ExjzzU9wV2kdyzAj187h//wWUrXLmb6ihpyqipIW1iFFgmB\nd5zzZ7t5oSXInqHQpDqfP4j+8DivHL8AZhs2m4WZpcmsv3sR6nQTzlMezg5pePMtbPrkDES7DRBw\nGGI4jJM/xX3kRd0Z8bKtuZmRX0V49AuLyFk/l3uTErAkWBBsdogpqB4Po/4QbU8d4JnXD/F4eAA5\njsk5mDBBCrb2E2jtI6FkOqHRHnbKg4xr1/7h/CAkQSTd7GB1fimpZgt1Wxs4vq0pbhU/l8PqJAsV\ndiuoKp7gGHWhAeQPYUp9VIkxhIwrUWJBbjLLZiRjvrOWkXEfR353lqferOOlUFdcrtUeC/HVgQuk\n/Ws/f2/IphAbAUFiUIR6KcqQGmAg4sInh4koMrISu6azadvHo3xz3xg/XHqGomnlmCpns3jOQhaj\nQTiEElDwBIIEWjrx7K3n9NZGHvPJnHd3E4vjfWloKKEYMY+MqKnkZCdTJWcw05zBLbevIuXBxYQG\nnLzxzW38rv80rbL7Q5kZG4xFOO3qp94lMPOJk/xN/xDL19YglM1GG+1Daz7NYy/0sqO1h4FQ/Bqw\n/hRN0xgKuXj1wCnCw6N89Z4K8ldvQFq+ntyVAjlyBDQNweIAyYAWCdI3qtHvnrw0f+RFXUPDEw2w\nt6eJm7/exsa0M/zVPDO1a0oRZ81A62whuvc0366T2dY7zGDQhXwNFlJHYJht5+1ktxhZtSqKIgdp\n8fTHxf3xcj1HLkWa0c76wjk89B/rSClJ59fKGCfkyzenigf595eRvGjCb0YNelBRJ+NJNSlUNBzX\nV5D3N0sQjEa0sR7+87/38urxswyG4mgUzsSxfjjg5lE8bxlvTfw3Lzqlax/iNNigEqVhsIf/938P\n8ffzdlG+tBhx5nQwmFHPnGb85R5+PWpiT8BFV2AETyhASInFVdAvEhiEsTMauffAw9/byKd7KxFT\nczBml6K5RvDueJrH/OfpUnzXNOzyfmhonPf08uVX+xC3HgdBnGgX1VRiMe2ahwhVTWUg6OL5xhCt\nPxnnqTEn6Zs2I+SVIBgt77zTvkZeHGvmZWXyodOPvKhfRNFU/OEI24abOHVAwN5wAcG6FyIhVG+Q\n3oCGOypfkwcWJkrpdrk7aHj2KRJ3v0IsFGTcF59Ez9XKgBGRNNGESVJBDhOMBAh+CMfbP0U53YRa\nsxCp1IziHcMXubKY9dVwLjDI+cgQd1pMxKJGmr9dR/3pFoZDXq7eSeP9kd+S8L8kqqbiiQZ5daiB\n4/tUzHWdCNajIAhowSCx8QgjMQGvqhBRY+8c0hFndod8JAw08/2mYxjK52Esm4Xq7KHz6S28vqOT\nZ7qP0+IKxLW89UrQ0IgqGih/mXDpxDCQMPXDQ2x6ysv/OfJbFn/mVhwr5iJIBohFUUf7+I+f7uLF\ng424IpNvlvzYiPpFXLEgLg/gCRA3v9DLQNU03EoY9/AQDH/w5z9MrJpASUxCVFXQVNKNDtKMjri7\nUv45fniyl+PabsSkBI61NhC9ypFcV4IvFmLn8XOkfsfEci2dP1zooScY3xj2RxVFUxmTA4x5AE8I\nJlkxcbWMxKLs63by8981kJrmRjWY6Bhto7G5g+YON42R+EwB+zijahr+aIS6AZnvjZ+mUPaR9OYh\nUswJ2DHQOtLC/mOt9I6MX9XpQdA+jODWJZCMuX+pS08pskQrt6SWsPC++azKS+HVvR386mA9Td74\ndQpeDgWWVAD6wq4PMQgxgUkyUGRMYJmYzEEhwEDEQ+gynRd14oNNMlFiyyRZNKJq0B1xMRT1XtO8\nwscZURCwGswkGW3YRRMdAecVibkiv/9cWV3UpwiSIFJgT+ezmUY6ombecPvp9l+e8+BU5GK8e8Ju\nVSQWhyntOjofFQQEYvL7b9p0UZ+CpFgcANfMV+PjgCiIiIKA3WjBbDAyHvKhaOplV11cbfJaJ/7o\nf5MJBASMkoFQuPv9//0vKeo6Ojo6OvElfpMjdHR0dHT+4uiirqOjozOF0EVdR0dHZwqhi7qOjo7O\nFEIXdR0dHZ0phC7qOjo6OlMIXdR1dHR0phC6qOvo6OhMIXRR19HR0ZlC6KKuo6OjM4XQRV1HR0dn\nCqGLuo6Ojs4UQhd1HR0dnSmELuo6Ojo6Uwhd1HV0dHSmELqo6+jo6EwhdFHX0dHRmULooq6jo6Mz\nhdBFXUdHR2cKoYu6jo6OzhRCF3UdHR2dKYQu6jo6OjpTCF3UdXR0dKYQuqjr6OjoTCF0UdfR0dGZ\nQuiirqOjozOF+P+eSR1jW4nLvQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fee3e309510\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time since start: 3.58 min\n",
            "Trained from step 4500 to 5000 in 32.00 steps / sec\n",
            "Average discriminator output on Real: 197.43  Fake: 191.78\n",
            "Inception Score: 7.44 / 8.38  Frechet Distance: 55.80\n"
          ]
        },
        {
          "data": {
            "image/png": 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63nWUzDKix3iq7RAv/MkJvuVu4JY/v5Wbbt2AT9G56TuDk77DDTi9VBSpFJW/\nsTtJxlFMC+kDiKys9uSz4IG1VN21CAwds7+H5OHDpEJjkx7UMKvUy7//3nzkujmYXU0kt+1CLijH\nNX3BePkMxpPwEAKhOij3FvJAYCZ/nz5AaBLcth9JUTcsk7gJx/UE3w4149lhsah1Gw886GPxVQ2s\nd9/K1m8m2D2SudovkhCUeHLx33Mb8pwGkruO0PPQ0xwfa89I+7C0oaObJmERZ0siioQgZRuY2CzN\n9/KdVRUEGxaCZXLi8ePsf+IkI8nM1vooUL3cXDWP+75xPSWNDYhADvZwN2e27+Zvv7uTQ70X6Dcm\n393gUZw05hbzv2YJZj/wSZyLG8frhkViWOc68CtOQopJykxj2eMRD37ZyRd8s6iyBQ33zaf0ujk4\nFCejPZ08+SdHiUfeXdTlhgaUq65COH3YehqrqRkxPIo90guag5TlZOO3T/JY91Fa+/sYjcWY7lHx\n5OhIfi9xOUyaD0cziJSR5nERojLWy3XOmYiaGcCOSR8nlIwRLSvDsXo66Drm68+zq/so7Rlyxb0b\nn/MJlvu9CIeL5Mk2mr7xU77cfoKTI5FJNTokIVD8uYg5S0AS9P3oKE/u6yawWOf+htcRucVI5TMR\nijqeiS0kCmeXccvX5rD3XyJs7WliTJ/YzuEjKeqmZXLYGKOLJF1GDBEWDLc04fiZGwzB7PkFfOmW\nMo78vAU9mRlR90oyXwsGmFZRjdBkmntDbGpPEc/QYaQN49s1G6L8x3eqcuayqmEBDb9/PbLTSeQX\nm9j02gG2DXZmNDXcr7pZWlPG5++oomzBdJRADuhJhve1cfinx9l+4QKj6eikWX8CgUvVcMkas4v9\n/OV1dSzYsB7n7LkIv2/8mvwicu/6NP+0KkksEcMc6cZORBGeAI6CKmaoOXgSUfx1eUjDQ8Re24k5\nmIDU+/Ane3xIBaUIJIwjJ3hsxyCnE71E1RMkLZNY2qDj1Cjn46PE3ohmaU7a/GVbP3972sPVZprj\nKnQI8YElAcH47sqrubg5V2Jm0IMdjWFfaB4PNJjkeZW5cqgorUOungaGgdnazVBohNh77IomE7eQ\nedBdy5p715GzoA5Mg6FQmFc645waik76WZMsZByqAzQN9BSBOU6utNxowSSEQ0h1CxHyeFEzKzYG\n6QSyy0XpupX8N3cRnzixhyO7+jjSmqDDjjNqxBhKXFxEzEdS1G2g24jRZyXe+H+boXSEXaebmNPc\nwIprZrIbG2qVAAAgAElEQVTo+uX4f32IaGpyt1cCQY7iYmVeBdffu5a88iKM0+dpOnySHdHolPoK\nZUliblWQG9fVITfMQN/6Ks+/to+XLrRO6sHPW8nVvKypKuS+ddOYc+0yJH8QgOZdzWx+ah8bjzUx\nmpo8QQdocBeyalEVdXMqqaooY9XiMqSGBaBqYBrjRcWMFK7Z87hqNtimgR0ZgUQE27YglUY/0c7r\nvTHaLrQw1nSeyL6ThBKChP4+SgYIMV46V8jgdHByxOaV8wMMpkKkDP1tyw4M6QbPD4aZfyjENaV+\n7tGDlLernIubbEv1v80gl84KZzFB2ckoJl1WnJ7k6LghgA32+LmMR3YwPVjI9dc2cFv9NMrrSmg+\n38Pr+7ozsm4VSUbxBBD+fAzdpu2sRjhiTlkzG4HA53Zxx63zqbhmGUpZEfZAN/1nD/PrSD/JDMzD\nqzjId/rHy1GbJp6V85hbXzxeT7+gEBQN68xBXj82RmggRK0rxZxpfpTp9SxcWo09zcns6XGu6Dfo\nt5NE9BijF47y060n6Rh6f1FKH0lRhzfiUM3/2M4KBMIabxUm/Hk46xex0l3Ma2NxRibJevYrLmq8\nDpaWFXDNFVdTcN86JDvOyd3H2LH/GOcTU7utnKG5uXp2BYvX12Im07Q9d5DHz/dwJB7LmJUuCcHS\n4iCfXFnPdTcsQ6qdPx6qNtTFrk37eWTHAY6m+ifdP1vjyufOdctYc8dSpNzScYE1dMwzZ2nqHaZj\naAgjFcPOKUFTNEoUL0JIOPQ0SmSE9o5TjG49xk86UxyPxgilYxdV/6Wra4jDB06xsL4Ief585rRI\ntEWS6F1JhsTb32vbtomkDX7cEmfJ8lKub6hg/uEULx7qZHvqnetrXwqzXIWsrCnFKnHTJKVoCXVi\nC7CT8fGKpk4PQU8ui4um8fFPzMSlqpw5184vt57mqTNjGYkYs7HHq1RKMnoajnR5CCXElAUy+mWN\nxmAJNZ9YhKu6BCSZwQs9HNp1lCPRrox853zZTa2aA7aNkCQoqUH4cgn1hultSzF25gj9B1/h0a39\nDI3YLMizWT3diWteG/7S6eQ4gxQG/awrAznHBbn5jDwZ57W95+ngMhf130YgkCSJuWous5VcEAKX\npHK/VsFJ0c0IExd1RZKZX1jCZ2YWcdvaarSPX401lqLr2D4ePtHCLwdjJIyp21YKBHfkF3DHzEak\nqtlEz7ez8ayT5pF4RueRp3q4dV4+1163CGXJ+vHwzXiInoO72dVygBOJvow8LINmnHZTpS0qIccG\nEaaBHhsl/sgv+MHeC2wcTBIzkqQMnRynl/XOSlRZI9e08Zsmz0khepOjjCQvzYe6fd8ZpJEQ3/rE\nPKqu+Rif/rSfGs3Pi9tOsz/Uzbm+IdK2Rdo2MKz/SPO2sYmlBrByFuO+Zh3uvEE4dGHS79FRO8ri\nuTKr19ew1l+AFg8TcApEagzh9SNyi8GZg55SGUgnSGz6Nd/feJwnT/YTTmcmUixHcpFjSdixEHpK\n5ZCUICym5lxBEoJKT4AHahfgqWlEON2Yg4McPtrPrw5krlSEz5YIRHVami8gF9rjBexaT9G0uYUt\n28OctiLsHT1P+o0qkAfGBD9pU8jZ1ku18yRzJB8r3UkaipK45hXBinWcfvgcY33vP379shB1GxvT\nMsfdLEKAkDAEHNRkwtLkhJHlO/3ct76UO+66EnXmYsyREQa++gP+qKmZnaEQyfezhZ9EvJqTwjXF\nBFcUYHV1Mfa9f+MXI0P0W5kN5bzZW8u8NXegLFsKCEjHMfa+wNe/v52XTnRmLP38yFgbX/qHf8P7\nby5ynV68spPO2BCJRJy0aY73eHyD/tgYv4yFGH/1jbevM2BCD3IkneDFMxc4+u0BdpQUUrBgBSvv\nnsuyZQ4uHCvip48P0WonOBPvpS85RsIY76aUq8o8UV9Ow7KrEMFczqdb2alKE74fb+XwWCt/9ose\nyjYeZaanhHmam8/UDpJz1zqkqmKIR4lv2sOZR1r4amKQC5EhIqkEaTNzIttvRumPDWC1nybRdI7X\nR04xMgXF1MYRFNQGue6vF6N5NcAm/uwmzjzxPPvCk+v6+m2OJvs5sf815AdeH28IA+MJaKaFZb7Z\nYOY/1qGNTdrU6Y+NMRgLcQjBo8JG6gQOt8JPD2DpJvpFRPJdFqIOMDdQyQ33r6Px441gGSSiwzwf\nPsvQJJUdHU5G+NfXz/CL433o6tMkEzH0jgFak0mSpjmluXGarDLLU0rJrFWIynr6T3Xz4gk/I6ne\nCbVBey8kIXHVtV7qZ3tB0bD1FEZvJ4OPnaWzpZdIKnM1dyxs0rrOmGEQiY3Xw05b+juel5hveJQn\nE9OyGRhL8Mf/spO/+bKfGbNqkOvnUFNYxRcXGqRkhYRtoGNjGTrYJorTR0N+IVq+l82/PMijT+xg\nd3jyC1lZ2ERTKVr1AXrDo+wTEs8OGMhNwwiXEywLKxIjMZii3dJJTUHVxv5EiO+/uptnDx7BSCRo\nHY1hZLjB9Zss0vK5L3cuakX9eAOXyAgdvTYdww4MO3NzsADLstAvoSb+bwT/zaVr2W984sVxWYi6\nEIJ7ZjpYPS8XX46K1dOKfmA3XdEhkpO0iHTL4NzAGC2DoXF//hSW53wriiRz1/X1zF9Yg903QM+W\nTTw9PMBYKpGxeQnApWjkVwVx57rBSDN6rot9P9rBUwebOBeOZD4vgPGGC5ZtTKAv0MRIGybbzrby\njYde4J7yetYsaiB3WT6lwSTC6cLWDYTmBB3seBJDSnH66VO8MtrKzqNNnGrtmHDI2jthY5OydFKW\nTgjoSQPhqSlu93bolkH3SIjukanvumTIMimnC+H0gQBj13Y2nz7IlsTwBxp9NBVcFqIO0B4d45kd\nB5DOdWOHR4icPU04Mbm+s6msbPduGJbJ+dAgT2/eiTUwQuue/RxOJEiYk5vJ+laEEFjDYazhQToG\n02z+9UFefGEnm+ID77t110cdG5twOs4Le88Q1fo52dpJTlshdjyK0DRsPT3ess80sBNxDFuhadMw\nW6J99OrRSUkDz/LedBlRNradIfnwc5i2hb5zC8+fbaM5Qy/UDxPCnup83d9CVks/qKGzXAKarPCN\neXnMXb2Iw8MSv3zuKKciU1e7PkuWi0EgUGU5Y3VtPmhMvedt//5fWtTFG7U4prpaXJYsWbJMlHcS\n9cvG/XIpZMU8S5YslxuTH1uVJUuWLFk+MLKiniVLliyXEf+l3S8Xi0vRKJE9TPO5KK6RsXWD3g5o\nikToM+LZyIYPKQKBIskYlpl1uf0Xp6EkQG1BgD1nhwmlM9+E/IMgK+rvE7/sZEZNNRuKa7i+tIAF\n1zohnmD/Vounu7o5NHSeof5+zkf1KW8AkOXtcckauZJCvteFv6aUhJHGDI/QNRRmKJqa8sdZIJAl\nCQmBYVtTEtef5T/wyw5umV/HDUun8fl/3UVET2Q0We+DIivq7wMZwar8er78x/ex5rZl2AhMc7x4\n2PIbLZZbaYy9O9jxyBPcu6OHSCIxJYIhIZAlgZDHk+GxbWxzvDzv1Oa4vjuKLCFJErYNhjE11rIi\nJGb6S7nbn88dC6sp+4ffG+8cv/sF/uKHm3ho5wWSF1HQ61KREEiAJEs4NQd+hxuHpDIaizCail2W\nluKHlVX+KlYUzyIdcNKXGL1sX6pZUX8P3ELh93Pmc89f38WMdXOww0M07zvGT35whJm2i9kkmXZv\nI4E1M1n23/+UZ9ft4vb//TRjscz2YNRklRvcldyxdjZz7qwHlx97qJOeH5/iqdZBXkmN0hsf+cB3\nDU5F42tfuJXbb1pDU/Mgf/eNRzkZas94ItcdgRncc1MDi66sJ1BdP16HQ3EgLd7AAzsj6MdCfH8s\nc32IJCEIqB7m+8q403Az7w/mELhiHnKwgOiJLvZ9/Vf8lX6asDV1ReDeDoHAqWgUuAL0x0dJm8Zl\n+6JZcWMly1d4OHy6mVh66ndqU8VlI+qarCIJgWGZaJJCuTsfVUjcVWxQVepAriomZAbZ91wH241+\nehMh9PdRQkAFrpAkqmIdvP6LVrYcb+dCVxunTw0QRCaISVGyjZuSV3PXzcuZPncu6/NPsyV1jrEM\nFi+61l3JvXdtYNXtS8ipywNZheR0KvLnUnD0AKt2nmX/kSAv2iH6EmPv67tmgmtclawub6ChoZZA\nQuNTso9vyRqjdjJj6dqykHDIGrmyQXCsHWNHO4lRF77P3obk81O1uJjq00HknZPT6PetqJLMckcO\nd1VVMPNj9dTWLSN3bjFaUS5oLtJuF84HFzLt4RGahvtJZLgJsxCCCkcOS135zLdNjgsHFYbFtMVe\ngo1FyLmFCFPl2Yf281r/OYYnqV7ShwkhBN7KckQqyNCWwSm10oUQlDiCzHEWMENz0nhrPlpNA8KX\nC7ZN+nwnXT/byv8d7SMyCYlSH3lRVyWZfEeAStXP7IBBcV0OjpmzKMwpQxYS64MGRfkKUlEuMcvL\n3LIeFvUe4+HXj3G2d/A9a2qnbIuXYl0cfT7G4f4YR9pHCOlxTMuk841rHCcjlO4JcOfiUrS8chY5\nizkktTFGBkX92vlccesScufVjndSAXD78V6Rz5xKN9W19cxaNkbVQDMPbzxM+9ggKWtqK6YIBBvK\nFOrzHAhFJaDarMgzmWbmcTI8QCJDHXBsbI4m+vnZ0QSb2y38owZrqGT6/RaSkFAdBoqmZ8xS02SV\nWfXF3HLjQvJuXIBUPh1kBSQJISS0giDlNy/jD3STf39mG6c6ui+qtvvFIEsSqxwBrmmczaIr5lCr\nJ1nqDFCkuimZ6cVblwu+AHpSJ2cgzsmnuhnunzxRl4SET3FS7cihwZQpKU7iKvdimzbdHWO8OKgz\nlo5lfEfplDVUl5fmmMTm9sz17X0rsiSx3uvlynn1zFi8iEp/Hg3rClFKKhBuH2Cjd/TQaSm88osd\nHBnuIjbB/g8fSVGXhUSFqtGgOnC6NKY1zqQuWMnyIpu6xeUoSxYhFA3zfBvRYWgxbeL9AjdQUldE\nmVrGDn8HbQOh93yYklj8LNELO3rf8ZqUqZPobsdqOo26YhrzAzo+xYYMaJaMoMqZy7wb51E4q3S8\n16FtY9smJGPYpoFUWIa/bBrz1yWZ1lpOvHOAhw+G6IpMrairskL9ogB5FR4QgrQwGXJbqNL4ripT\nWLbNiXgfTSeGEEIw2+tj1fIchKoBkOxMkOhKZERICjUf8+fUc8XNi8i7YTFS6XSwTWzLQCBjS4Dm\nxFNZxyc/7WZsbJhHXo5yumdo0ucCME8N8sl5c7nxjlUErpkN6QSl7gCSJzi+dt7IqpbSCVasziGw\nWYGLqEyrSQqz8hxUFAXQJTejZ0NItkWg2EbL96L4c8jx5jPXX81iU6W2JoqnIQdbtzh3qgf13Bi/\n3nGAUCKW0UJbDkVFkRTOWzrbzKnZibgUB8tnFPO56XVsWLsI1/oFCG8uQnMhfmv9q5VlFH3yKu6N\nanhf2c7BvnaGzEs3CD9yoq5JCuX5OdxTXMIDOfmoAYWcz61Crq5HOL2QNkiFIgx1tjD6k0e5cEJl\nawK6FUEpGpWGjNuyGLCiWJPZCDiexhqJoKAztzSMt9mADKwdp6Jyd91civPLQHMBYFsmZnSMrnNN\nGNExnJ48gmWVeIry8VXP4Gs3V7Oro53eaGLKwi4lIch3+nCtWolcU40dS9DdOcojXRpHxloyZqW/\niY1NytSRhUROqZtZ95aguFXsVILuDouensld+oLxCKmramby+c/fwbIbFiMUbfzFkUqCmSYV1jGT\nNrJTw1HoQyqo4ot3L+VCdydne0cm3SUgCcFnyqZz9X3XEbxpwXihMRjfMSDgzb6klomZiNHT1kcq\ncXG/S5HTzx8vr+Gu6+cTlks48b0zaLbNjHUm/sVViOo6RE4pki8PbBMsa9xVKASzr07wvzpaOXa6\nnTM9nZk/uJYVTKSM9u59E4essKA4n/99/zrmrVuLVFyNkNXfveiNl5hQHXjKKnngr++kQdf48ZZX\neG2oleHkpQUVfOREfbq/nK99ajU3374KtagagT1eHS82itXfirH/IKdfOso/ntXYNtBGPJ3CsMdv\njfhNtZfx6BBrkjbgspBQK0tRFs0hbalsOlvIcKyFyVZ1gcDvc/K5L8+guDYPIY//fHZoiOHtG/m9\n/7uDnlCYJc4y7rt9Ddd+cS2SLxd5wy0U/boHb2eUUDo2qXN6J1Qhs9BbSaCgFuHyEX9lL83/8jDP\nDrViTKE/M+jwUFFSj9y4GlQHducZXgv1sMme3IqWTknl3vxGvvCl9dSvrh0XLsbF3tac2KNjtH5v\nG10vNBFoLGfxP9+C8OUjVc1GzduLIp2e1IYVAoHf4WHm19ZRsH42wuF++wttGzs2xuiJ/fzRj07T\n1Htx62Olq4K6NbejXr+EPKGwZsN6EBKSDEjjLw5sCzudxBpohcgIoqASKa8cIUlI9ngTc1nIkMGC\nyrplYBppSrQgtc58umKZ2RnBuB6U+j385I4qSpevQCqqAuntpPbN9SfGb5PqZPlf30jj/DgPP5ng\nb44MEdMvvnTyR0bUBYIcp5ev/tltbNiwGC0vgJ0YJXX2BOd+2MZT8UGakkOMDPcxNDhCT0IiYiTf\nItyZ2d5Zto3ldIM3gBkdZdC2yITH1iErlPgLcc5ZjuQNYNs29mgfp/bs42++u5eTPT0kDZ3NkThV\nW6KszQvjfuCTCH8Bd6ll9Mvn2S4yb61LQiKoOLlWd5InO7HTSTYPDPKPfaNTKugA04WXq9QSRKAQ\nhETs+QO0HDxKR2J40saoUXw8mNvAtVdC1axqZF8OAvsNtxjYkREOfnsHP3l1B3uGOsnd38KVfx7n\nT775CbwBH4uD0zjkKmFPtGPS5iTEeLKcll+M9IbvFssa78ITGYbhbuKbT9BxaIRtwwY/GzhCc+cw\nMf3i/LmbwxdY9dSTzG7fiTa9GKmxEdJJTM31mx3KSGeSbT9t5heRMwQUJ5+6soY1116BqKjHSkaJ\nGZlfk5XOPILILCpO88nFEttfERmL8vEqTmblVONdvhY5vxhkFSEEtm1hJ6KYW57lH1/p4txgivXT\nA3zi+tkoS68ez2NwufFcexMfi7tJdz3D13t6LnqeHwlRl4QgV3PwF7V+1sytIpjjYWjfeQ68cIDn\nuo8wcGiYM0aSYTtF0khPyfbqt/GqTry+PFAdpFpOszfRRSgDoWoeSWOmqwjVmweSTNfGY2zbspNn\n246zvbmDWDqJDYxacfb3hth0NsLHLBOhqMzymhRqJvYU9ExwySp1+SWsuHcegTI/Pa82cWjjMU7G\n3l/j3MnCqahMX1TNFXcvBEUFM81jTQPs7hud1IPJXBWuLNGouf0GtLIy7HQC20iDbWMODTL084M8\numk3r/S202vGcI4lSO3dy+dfcOC54RZWXFHGiaYS9m/umjRxEwgCihtFdSIkGTsdJ9zUxclfHue1\nRDux2AiRli6GuiK0JWxOpUcuSeQG9SgPnzzH7u525DwPoug4mMb4wbBpgGWSCJu0nxjhlDFGwOHh\nyqUl4HDwxj4Gw7YyHkY5mIoQ7T2Pv6aKslnlSK+dxbQyM6Zhm0TsNOQVIjQHArDGBjh/4hQ/fP4w\nY02H2dUSYiRu0t7qIyXBZxdfBZIYb1adk0t+QR7TXPIljf+hF3UhBJWFOXxy/Tw+PrOSYEnR+N9t\nHTUdx9Hdz4ikEcMkYaQ/kEYWxZqfEmcu8ZDFyZebORnpJ5aBSJMCp+CmEhmHIrD1FHsOHueR53aw\nK9n3nw6ZLiQMdowluDkdB9lHTkEKr89GjkgZD+cq8Wnct7iY6lsXouU42XfsHLuPnSM5hY25JSGx\npjqXG69soGT1dDBNrLbTbOrrozkxeSGEqiQTLM2j6o4lqI1LQRXYkWHs3k66mtt45uB5RjY28fJI\nL71vRDWkTJ3u8BC9z58ksHQDZRUOyiudSEKaVIs1bRlYbzbBtmFsKMzW5w7waPQsEWvcAJporXEb\nm4ORBAcjCegYA7rf9Xq/5kbkFo//Z5lY4RGiqVjGLfWRdJSxky2ky9xIOUGcskbMyoyFk7YM+pNj\nmJFRbD2Frac4feQYP3/iVX7yynEiqfhvXmG6qeEUNvx23ICeoiepczwtX54+9TzZyfrqafz3B29C\nFFa/YXUkyZ2dz/q8ZSzdYvFiS4Dd0W5am1u40N1L60VuISfK7FwPszwSA+0D/PqVHkaiMcxJ7gQk\n+P/bu8/gOM4zweP/t8NEDAYZRCACCZAAQYIBpJgsKlC0JIqyRUXLVjhbOu/urbW73gtb3juffbe+\n0ganqnVYW9LqrJMsyQpW5FIkxSSKOYMgEokcB5gBZjB5prvvwyh5LYoSOQOxUP2r4jdOdZPT8/Qb\nnud5oShb4cZlFiyKwAj7ORoe4kji44/nmtQiDARH0b1DSKXzsFVYseVZUEYkEhn8/ThkCw0lRdy3\naT5KaSWGZ5Ajvi6OJScyd9GPkWdxcseqem5eswBhd5GY8nN46w76hgbStiFnkRXm5rpYtbwW1/23\ngKyS7GzDM+xlsKWFPTv38N1DH5c1ZaBpBgGPQjIuUO2p6mSRxowgCcjWBcr7j4ZqReRkYZTqhNoi\nTMVi015kpEoKq+rnUFldjXDkEB31MHC0C18wkPHBmIHBWFsAb00Mtb6QXGsWkWQsIxk3SUNnKhzE\nf6iDnFlzkS1JOk90sevtHoQOc9UcnBhkVeazeV0N965vQEgfjsqNsJ9Wn59tl5h5ecUH9UY1j9td\ntRhZ+QQmQ8THejGiQbA6UfJKcT74Tb7ikPlKIoLvibf4zXPb+fv+XqaM6Unfc0oW1i+ysWaWlyPn\nx3gj1oc/GUn7wyIJCTXPjbpyEagKxvgYkaDvgqPfuJYk7B/D6DgBs+aizC1Fz80iqWfuJHWLkJnn\nKmDN/CaUazYhLFaie3fib2u+pA2fy7HIWUrZ4muR6peRjEXpOdfFQ48fZGAsfedlljlcPLC0km99\nsQZUG3rQx8Tzr/D6O6M8PTDJ0akLpcEKrBbB3NVx7G4dFBeGPSttKZaqEJRZbHy9sI5S1Q6aBkJQ\nPsvGw5ty2DLo5mzCm7bzey9GQZCjqhQUFfHtP1vP8tXzELKMbzjG2/9viFg08/dhGAatETsdjhqK\n5syhxr6T0dAEOul/mRiGQSgQ4fivOsldtJzchkKW5M7moZIGtooJbpZnMU9LMv9b11F424oPs5Le\n+6wxMYpvvJu+mO+Srn/FB/UcNMpCY/hP7OeRn5/g9HAfoUQEISSqbHk8OHsJd/5NI/aaOnLuu4HN\nFjfJn/6e7wXbM35viiTztew6mpZcg26NEtizneGQLyNvfyEEUjiC3t4OTTdiJOLwCaMb3TBIBENo\n53uR12pQVEJefgm51hE8kcwcBHxtViUPLiln/fpshMONEY/Qukfg6Z7eDs+qpHBD0kKtZANJoaWl\nk7/+y18x7PWldZr/wGyFB9Y1Il+9EfQkxkAb/3pe54WBETqCF86uyFEdrCysxbp0HiLLgTHSjTE5\nnpZsLIFgicPBL+dXU/6nC3G4p9CGOxBWByI7B/dNX+U7zyT47uRR2vTAZV/v02i0Ofjn2jpKHn2A\n/JpqhNMFCHJEgrWWAOo0zRgGiTMo4qxUJb6TLXO3F/wZunQQnUe1fqo9HTTV51O+eTVf2bCU2+Jh\nbI5cVFlBybKCYvnDD+o6SII4OlOXWJF+xQf1fTEff956AutP2jjVNUEg9uFOuV8K85OJAK/9oJXv\n37KAhvXrKVlXScPoPKTHOzO6diwJiVJHHl98cDFzV5bx9rFW/qnDm7FppKbrxIIREt1eLNp7uc/J\nT56NCFlCOGypfOS+PvyeIQKJzFS5SkJikS3JsrpqstdejxGaIvr0U/z67FEORS5txHEpBAK7amXB\nHeUULcrBCE4Q6DtL82APiTSeVVlgzybPsGPDjnC4iAbCvP6z07x5uoPzIR+JT1h+q3QY/PUCgWvx\nSnBmM7pvkpF3J9DT8Oyss7n51oIG5v7ZWuT4ML/85RZODE6wti6Hr99yFZaaZay8yYL79+IzFRl9\nWrKQmOOaxTIpl0pdpmppHg23LWBB7TKs9bMRqprqwwPENJmhqAPNyFwh2kf59DA+PYLQZUTIhiIp\nCD0zre80Q6c7PMFjz5wEVwXLry/G6SrEaeip9EYhPna5Te86ybMvvcMz21qJX+T3fSFXZFCXhJRK\n/wE8ehzPpBfJL/5oBBzWE7SHvfSdmmKVbCerYpzKpRVk1ZcgSxJ6GvN+P6pCsXJvfjFV62aztN7G\nzqMneeKtZo77M1epZmDQH0zyZHuUhxJJrK48StxllNh6GYlOfrA+qkoympFao9XDOpG2EEo8gffw\nJBM9UxkpR5eExApnGU0b1jJr42qQZAI73uSJrSfZPTbCuJ75PQ5JCBRJIc9p55Fbl7Lo1jVYK4qJ\ntfXj33aM4HuZQelgUyxcZS1mzsqrUFcsxZj0Ety7k+cOH6fd5yF6kU1yV4GLhTcuRS2bg1BUurxx\nun3Jy7o/m2JhY5WLr86fzeraYmITgzy99xy/PzbEOW+AQr0AY+VspAUqOU2zUHZY0h7UBeB2OvjL\nb2xgQWE1+bKV/NlO8pbMQriLMJIJ9KkxhNUJtiysDp2SOXHkSTKZov6BkBYjpMVQCx3k3zIX9fnz\nEIp/UASUbjEtwdstnTQ9v495igP3hqYPakv+iGEABuP/1s7hbS2c6Ru95Bn/FRnUU1LpTu+70D9Q\nkJpuj583CI1poKgIRzaKJKd1ZAbgUu3Mz7ZyU2UB/7FpFdmFY+xpPsfju8+x++zgp5ra51tcVAgr\ncSPJOS30mYLsUDTJ072T3Dt4HmtJCWvWrCA8FOTwsZMciIaotOYSERoT8RBRLYGu6RgxDQyD7h7w\neo20l8ZbkLjels+mdVex9I7rsVa5GT18kDdfepvHOrwMRTK/XmqTVWptdpbMyqdkbQMP37MK19wG\nSPJ3ARIAABBoSURBVMTpPHmW3QfOpW1TUBKCAms2X1w0m/oNK1Hr5jB2toUtr+zi4EQvUxfp25Gl\n2CgqLENpWoJwZmP4PTSHRmhOXlq6pypkGq0F1Dfkcv+6GlZVZOMZGeWN107wLycDeCIRbLKK0+oE\nixUtqTPQrRCLpH90LITAabfypY2NFNYsQFgdqectEiLS2cyWk/2EfKNctWoZdQsbQIBQp2+z1iKU\n1J+ibIpuW0711lMEIv2EM1jdPBSdYPu7x6hyKqwvVlEWNaSWWDBAUj/o22RoSfTeNt7a08KJrpHL\navJ2xQV1CUGJ7MBqwLgeI3CRDU9JSBTZ3Sx3CUocCkgKQtNQJPnfvRYuj0CwqLSEh5eWc8faKqTV\na/H86HF+caCP5okws6023OqH02fhVBGqApIMsgSSjKRYqcut5Jq4hfOeEX7l6f5MQT2mJRiYHKV3\n9zs4btvMuo3LabLL7Iol6Gvu4Dp3DafjHkJajHAyBk6Bc7ELocqck1TGRPrXtrMtFv6msYlF37we\nx8ISEidPcua1nfyvQ2OMhwMZz7Aokx3MKSvky5UlfHXZfNwP3YhwZEMwwNCp47x24jhPBdK3hyAh\nUWDNZvmNJZTMz8IITtDVcZ5/POhjMvrJVaoO2UJDdh5riksR2QUgBN4z7RztaqEt4vnM92IVMlWO\nPP60cQ0b7imlcOUixtp7eOuld/nbQyNEEjEsssKSokLqq6sQhUVER0fY8mwn46PpX4YzSPXLH+z2\n4soaRRUWAqEkY55Bet95k//yzEm8wSg//H4W8xvqicRlej1ZaPr0LL84JAsOoYBqw16xgFurqhn2\nj9MTzGyq7daoF8uhd3Bb/RR8/X4qXQZybj5SVg7IMoamkQj4OPPqy/yy7zQn4pOXdb0rLqg7JIVH\ncpcwV8g8OdXJlsjQJ/59RUhU2/OpXauSUy1jTIxhdLVhlVWCaQzrTouFr90yj3vuvRlRWEH4fAf7\nWovIV+DGYoUNuRobigKp8mgMLEvKkEtyEVkucGaBzYGeU4rhLkRrP8Fjz25l8tXPXrIfCif52RN9\nfGdFmNqSMrK+uJJ52dls+PbzlBsqB+NhIu9lxBgYGHqqt4dXJAmL9C5HqZJMQUEutT+6C2d5BVrQ\nj/eUl669EcbCmdmM/SiLpPCIu47Ndy6m/M4mREkNJJMY0RixXa/z5EvN/OvxQaZi6VsWMzCI6nF0\nqw1kBcMzRKTjJMOf4tCFhqwyvlGdy90LVaT8MoxYhH976hxtxy6turXUms3Dc69i849vxzGrGLQE\nW7ed4bvHfEQSMSQhKMnK5T+sKeb2G+ZDbimT+3fy69BZBvX0LxUahsGEP8TPv7edv7h1P4XDU+w+\nm+A3/jj7JjpJ6hp2xYoqKwghkVcouOZmDfXXBhlsaPqB7tAY3RN9GOEATqedP7/Jwo5hCwNhOaMp\nlZqh88pIgN2vnmDNQS//cnsuefd8A9wFGIaBHpli9OxJ7nryGMO+y//dXHFB3SoZfKFgnKrbl7Hv\nUITsvZME4n/8AAohcChW5ucV8IPb86nddAuipIT+be0ceXEMfxpPlREIfrpxLhvXr0YqrwNZxl5T\nz02PV3P1exseTqHhkLTUelAiCvFJUOTUFDQwSWjvLn634yw7g6N0+QcY8l7a5mFc19gV6ORPYoFU\nT41knEDYx/5IP+OhCSa1KPH3UtWEqiJy3RhTXlqCA4wm0lfRKRA0Oqz885xi3DmFYOi0PX2cZ3+7\nh2eDvWm7zoVkSSr/zbWITd/dzKx18xG5LkjESHa14/nJTv6hu4u3RkbwRNJbxWqQWpvV7FmgWkCS\nQFUv+jlZSOQLlYJlq7Dccz1GNIz2zqts6T9AW+yz9yFxW5wsb6jkK3/ViL0gHwwd7fg+bGdOUmjJ\nJpqIMzurgB9863rWXduElJfLQMsATz82wPBEIGNV1zE9wWtjzbzzOwlF0wnHDPy68UHQLLK5cSo2\nwACrBVFWmprNkvllOosso/rH0HvOIC+8GuXGOzBe6UHvTl/LiAsxMCjWZL6mlODcdCdScXlqwzgR\npfNMF//wdzvwBdLTqfKKCuqykHDl5FDyF3eS01jF/ctXsHrJQSKH9pPwJvGP2ojGFfokBXeDm+XX\nVZJTMZ/58wpRc/MY3NLGay/s45nh82lbT8+2Kjy6vpob7vwyOXWLUqOzaAhCXlwlxbiEoPX1M7x2\n4AydWgCXYscbm8SuGbhkG+VyFmokzLaeZtr7kvRFpwgkI5c8MtAx8MaDxMZ6MaYq6T81xr7fHKEn\nNEY48WFBSbk1h2Uli5AWrsSITjEZ8xNJY+uCGyrdfHv9YhpuvQPFlYXecYQjnafY5RthOJnZnPT6\nXBv/Y001y268nZKrF2LJzwYtge7zED5znBe7fOwdHGcomv5KRcMwCCWjRPccRisvQ5lTQXbtchZm\n9XE60Pexy2l2xcqXbOVsvv06mu5eg1AE8cO7aX26l4FBP5FLyBdfKBxsdleQ19iAZLODoSPNnsOa\nzVZ+uC5ORFFwWpwsayjFJUVof7eVl18+xfPdZwknMrfcYAABLcqFVrzcih2bpqVaKUyFSZ7tTeXR\nT4O4luTsGR97Xu/l2oo6yHKzyF1Bh2WIwdjlLXlcTLHVzeq6etY8tAZrZRVYbak+PMM9+I+/y+mB\nQeLJ9MSsKyqoGxgk4kk8rUFyygLU15RQV7gevaGCpD9JaNJKPCEzioSzykFNYz648iAS4Pj2Vra/\n8i5bzpymLZGeL8gqqczJKWLT9U3k5znQWk7h64/SMZrkbHgI3ZFNIBHh3L5Omlu6GdDDOGQr/mQY\nq6TilCwUCAuKpnMiMU5C1z4s274MSU3j7PZOavPKsERC2PU/7OZmkRWWVefzpTVzELmlhF9+g5DH\nm7bRWakthzX19Vy9cS3KikaIhdn/djs7Ws/RGZvI6Cp6qeri+qpabr1rHcqaJQhHFug6hneY4eMn\neH17Fy96R+iPBTMypTYwCMaj7Dg+TPGyLuqqSihfUMefbFzI3i0Su0IjjLzXoEoAdklho62cr25a\ny/IvN5HjTjD8zlG2bj3FgaN99E+FMC7hxeMQKnkWNyK7MDXSNSSkkkrKC0soRyAsNgD0njYOvXua\n195qYduRc5yLeD/X4+oUISPpBmhJxvxxtp4ME09Oz/1ohk73WJCjbcNc4x1AmlVDuS0Xt2y7SHOD\ny2ORFZpqirn7S4sp2rAYYXcghIQ+OUzzsWZe2HmawXD6Wi9fUUFdNwwmpqZ48YktLPBWkLukjqra\nucybX4c8Mom9SCWoyRRpBglD5/jZIImYB8/QWba93MnOznP0JNJXjq5KMjmWLBJTdtr2ttDf1sG5\ns36Ojigc0H0kDQ1vNEhcS1zwh9Kdtrv5kIHB1t2duJ3ZXL3IzqIaifIjuQzHUsOjxS4Hm5bO5qrr\n5hCPxNi75RSjoxNo+uU/NHMtOVxbX8fKL6xGqmtEiyXo393Cc9vOsf/8CFMZyoMHsMoqSyvK2XTN\napRV16YCejJOqHWQvqNH2HnoIL8+MErX1AjJNLdp+Ki4luD1sTilh3vIqaxg1pJa7rx3JQv1QopH\nWuge7SOpa0g5brJsWdyft5jGry1HMnTa3zzE7gP7eeygj86p4Uu+z34jxtGJUa460oZtYSVYFIiG\nIRJCD8WIj8fojsj0Ht3Ky3s62NE+hifq/9zPH40bSTQtkQrqEYM3hhRi09iuaTwZptM/gNHfBeUN\nzJubTWGzJSNnH0BqmXKxy8FtTVWsu6kRyZ3qXWXEwgyc6uTNbc389vggk7H0tcS+ooI6QNjQ+FGo\nA/W5LtwvH+Gu1XN4eHkxkV2t+IcdHE44cSXAj8YxRSeoxTg0eT4j+dcxPcnAxBgnntjHjqSNHYFR\neiJe9GnoKncxu0J9RLYYxNpyaSjRua28hrf621HcKt+cP4s7Vy1Dn72QgWOneXQoSFf0wi+ez+Jm\nZzUP3LKShlub0G35jLT287v/s4Xt/ScZTmS2QjHXmkXTVVWsu7se4XRj+LyMejx0PrGHV7Yd5fmE\nB29keqok+8LjvLmzkyxbITfPyqegdgGN/3MeC3sr0U6eBkBaUIvIzkMqrmZ8LEDrz3by6tZDvJj0\n4Alf3myyPe7j5eb9rPq7YfL/6iakbCeGZxBjuB+t34vvmJcXxnN53dfJSHx6/k8+jaAWJRoPYSRj\nSBiousb05L6kTMSDtA9303skj9kVK2lcbKXkmJUPzqZMM1mSuasyjzsX1yFVNqRSPLUkU129bH3l\nDFt2nccXTe/xeldcUH9fQksyHvHz2O5TPLkX0FO9qfWPVJ+9H1rTddjFH92DnuR80Md9oQk0I3Ww\nxpVyAnlcS7A/0Ev0XIIHqeK/35Bg86sLqH6gnPybr0GtmEf/qX6e/ttttA73p+2kIRlQ8sqR8krp\nP97DY4/8Xx4bPHTR/Ox0sEoqNpsL7C6MwASxX/2Y7+3ysK3Xw3h4alp7tUeTcfYEuul5I8i5sWG+\n95/XIM1ZhjR/BdK8q1ItVCUJIxFH7zjAj/9pH68daWEgOE48DbMIwzA4GQ5yy5kzSP+pDUj168Yw\nEIaBrhlo9Ex7//qLSRgahsWOsGdTJid4MOlgHyITJz9eULMnwn1PtfDkzn/kp/44O8c+ezrpp6Xp\nGuq6xVivWZraGDV0dN8gO3+4hxf37edkMP0LP1dsUH9fUtdJ/sFzOb1h1QBiGT4U91IlDY2W0Civ\nnk+ygjoW/O/ryFpUhZyXw8SOMxz4xRZ+6zlNJI2zmFTdm4He187Qgbd4cewMQS0+Ld/KcNjHL17f\nwUuHj4KQMEaH6Qkk8MeT03ZM30dphs5AyMtThw6x99vtzCncy3+9qYx5KxoR2W48rUNs+00bT48c\nomPQhy8U+cT2AZ+VznvPZuxC3++V99zeIhdTr+aCJBPCoFO1ZKCl1ieLJZO0+Se4LzzFiGbgv8Ry\n/E/jupwaagvnIrJyUgF9YhLvo8/zanMbLdGxjDy3V3xQN32ykB7nWNDL98+1k/9yDHl7qllSuGuU\nvpYeBpOBtM5kdoT66H3uBdzbBaPdvQzG/BmbKf17cT3JwLiPgfHp6yVzMXE9yejUFGPtQTq7Jwl4\n28nfdQYsNsLjU5w/7aU5NnbFjZg/Lw7JiioUjHiUqbCfVhGbhmTGP2QAUS1JS5orzj9OTIKklgAt\nAaEAgZaD/P3hM+wb9zCZoSwxYWTiSPVPSVZLP69Lm0ymz8HVztncddMXKG0o5khXF6++cpTO0Mjn\nMtOaDiX2XDZ8YT4Ll9SAkJnqPM3Pt7UzGb789txa4uMLM82gbjKZpo0sJJY7y5CExInI8LSehjXT\nmEHdZDKZZpArMqibTCaTKb2m90gak8lkMmWUGdRNJpNpBjGDuslkMs0gZlA3mUymGcQM6iaTyTSD\nmEHdZDKZZhAzqJtMJtMMYgZ1k8lkmkHMoG4ymUwziBnUTSaTaQYxg7rJZDLNIGZQN5lMphnEDOom\nk8k0g5hB3WQymWYQM6ibTCbTDGIGdZPJZJpBzKBuMplMM4gZ1E0mk2kGMYO6yWQyzSBmUDeZTKYZ\nxAzqJpPJNIOYQd1kMplmEDOom0wm0wxiBnWTyWSaQcygbjKZTDOIGdRNJpNpBvn/zQzEhFvSDtwA\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fee24589910\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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GQkICrl27htWrV0OlUsHR0RHx8fEYOHAgAOhc1hElJZUWVSjc3fujqOimuWO0YIm5mEk/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\nnL3iwruMgAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fef6862e310\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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VFYmmzZ07F0lJSQaN12IxeHt7o7i4GHl5eQgICEBubi7KysrQvXt30XLjxo3D999/jzFj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\nJmLevHkICQlBXFwcTp48ibF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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fee200ff950\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Disable noisy output.\n",
        "tf.autograph.set_verbosity(0, False)\n",
        "\n",
        "import time\n",
        "steps_per_eval = 500 #@param\n",
        "max_train_steps = 5000 #@param\n",
        "batches_for_eval_metrics = 100 #@param\n",
        "\n",
        "# Used to track metrics.\n",
        "steps = []\n",
        "real_logits, fake_logits = [], []\n",
        "real_mnist_scores, mnist_scores, frechet_distances = [], [], []\n",
        "\n",
        "cur_step = 0\n",
        "start_time = time.time()\n",
        "while cur_step \u003c max_train_steps:\n",
        "  next_step = min(cur_step + steps_per_eval, max_train_steps)\n",
        "\n",
        "  start = time.time()\n",
        "  gan_estimator.train(input_fn, max_steps=next_step)\n",
        "  steps_taken = next_step - cur_step\n",
        "  time_taken = time.time() - start\n",
        "  print('Time since start: %.2f min' % ((time.time() - start_time) / 60.0))\n",
        "  print('Trained from step %i to %i in %.2f steps / sec' % (\n",
        "      cur_step, next_step, steps_taken / time_taken))\n",
        "  cur_step = next_step\n",
        "  \n",
        "  # Calculate some metrics.\n",
        "  metrics = gan_estimator.evaluate(input_fn, steps=batches_for_eval_metrics)\n",
        "  steps.append(cur_step)\n",
        "  real_logits.append(metrics['real_data_logits'])\n",
        "  fake_logits.append(metrics['gen_data_logits'])\n",
        "  real_mnist_scores.append(metrics['real_mnist_score'])\n",
        "  mnist_scores.append(metrics['mnist_score'])\n",
        "  frechet_distances.append(metrics['frechet_distance'])\n",
        "  print('Average discriminator output on Real: %.2f  Fake: %.2f' % (\n",
        "      real_logits[-1], fake_logits[-1]))\n",
        "  print('Inception Score: %.2f / %.2f  Frechet Distance: %.2f' % (\n",
        "      mnist_scores[-1], real_mnist_scores[-1], frechet_distances[-1]))\n",
        "  \n",
        "  # Vizualize some images.\n",
        "  iterator = gan_estimator.predict(\n",
        "      input_fn, hooks=[tf.train.StopAtStepHook(num_steps=21)])\n",
        "  try:\n",
        "    imgs = np.array([next(iterator) for _ in range(20)])\n",
        "  except StopIteration:\n",
        "    pass\n",
        "  tiled = tfgan.eval.python_image_grid(imgs, grid_shape=(2, 10))\n",
        "  plt.axis('off')\n",
        "  plt.imshow(np.squeeze(tiled))\n",
        "  plt.show()\n",
        "  \n",
        "  \n",
        "# Plot the metrics vs step.\n",
        "plt.title('MNIST Frechet distance per step')\n",
        "plt.plot(steps, frechet_distances)\n",
        "plt.figure()\n",
        "plt.title('MNIST Score per step')\n",
        "plt.plot(steps, mnist_scores)\n",
        "plt.plot(steps, real_mnist_scores)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uy1dsvWuwJeS"
      },
      "source": [
        "### Next steps\n",
        "\n",
        "Try [this colab notebook](https://github.com/tensorflow/gan) to train a GAN on Google's Cloud TPU use TF-GAN.\n",
        "\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "last_runtime": {
        "build_target": "//learning/brain/python/client:colab_notebook",
        "kind": "private"
      },
      "name": "TF-GAN Tutorial",
      "provenance": [
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          "file_id": "/piper/depot/tensorflow_gan/examples/colab_notebooks/tfgan_tutorial.ipynb",
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